Neural Network-based Content Inferencing Method and System

ABSTRACT

A neural network-based content inferencing method and system automatically performs interpretive inferences of content such as video that is associated with a media instance through the application of computer-implemented neural networks. Recommended objects are generated based, at least in part, on the interpretative inferences and are delivered to users. The recommended objects may be further generated based upon inferences of preferences from usage behaviors. User behaviors associated with users interacting with the recommended objects are accessed and elements of the media instance are selected for delivery to users based on an automatic analysis of the user behaviors.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 15/947,825, filed on Apr. 8, 2018, which is acontinuation of U.S. patent application Ser. No. 13/269,979, filed onOct. 10, 2011, which is a continuation of U.S. patent application Ser.No. 11/559,145, filed on Nov. 13, 2006, which is a continuation ofInternational Patent Application No. PCT/US2005/011951, filed on Apr. 8,2005, which claimed priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application Ser. No. 60/572,565, filed May 20, 2004,all of which are hereby incorporated by reference as if set forth hereinin their entirety.

FIELD OF THE INVENTION

This invention relates to applying machine learning to automaticallyinterpret forms of content such as video and automatically generatingadaptive recommendations that are based on the interpretations.

BACKGROUND OF THE INVENTION

It can be a time-wasting exercise for a user to determine the portion ofa computer-implemented content that is most relevant to her,particularly for content such as video, due to its sequential nature andthe relative opaqueness to the user of what lies ahead within thecontent. Thus, there is a technical need for a more effective way toidentify, and navigate to, the portion of content that is most relevantto a specific user.

SUMMARY OF THE INVENTION

In accordance with the embodiments described herein, a method and systemfor applying neural networks to facilitate the navigation andconsumption of video-based and/or other associated content formats isdisclosed. These capabilities may be embodied within an adaptiverecommendations system.

Other features and embodiments will become apparent from the followingdescription, from the drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B are block diagrams of process and organizationtopologies, according to the prior art;

FIGS. 2A and 2B are block diagrams of sub-processes and activities,according to the prior art;

FIG. 3 is a block diagram describing the relationship between a processand associated supporting content and computer applications, accordingto the prior art;

FIG. 4A is a block diagram of an adaptive process, according to someembodiments;

FIG. 4B is a detailed block diagram of the adaptive process of FIG. 4A,according to some embodiments;

FIG. 4C is a block diagram of an adaptive recombinant process, accordingto some embodiments;

FIG. 5 is a diagram of the process participant usage framework,according to some embodiments;

FIG. 6 is a diagram of process participant communities and associatedrelationships, according to some embodiments;

FIG. 7 is a block diagram of an adaptive system, according to someembodiments;

FIG. 8 is a block diagram contrasting the adaptive system of FIG. 7 witha non-adaptive system, according to some embodiments;

FIG. 9A is a block diagram of the structural aspect of the adaptivesystem of FIG. 7, according to some embodiments;

FIG. 9B is a block diagram of the content aspect of the adaptive systemof FIG. 7, according to some embodiments;

FIG. 9C is a block diagram of the usage aspect of the adaptive system ofFIG. 7, according to some embodiments;

FIG. 10 is a block diagram of the adaptive recommendations function usedby the adaptive system of FIG. 7, according to some embodiments;

FIG. 11 is a block diagram showing structural subsets generated by theadaptive recommendations function of FIG. 7, according to someembodiments;

FIG. 12 is a flow chart showing how recommendations of the adaptivesystem of FIG. 7 are generated, whether to support system navigation anduse or to update structural or content aspects of the adaptive system,according to some embodiments;

FIG. 13 is a block diagram of a fuzzy network selection operation,according to some embodiments;

FIG. 14 is a block diagram of the adaptive system of FIG. 7 in which thestructural aspect is a fuzzy network, according to some embodiments;

FIG. 15 is a block diagram of a structural aspect including multiplenetwork-based structures, according to some embodiments;

FIG. 16 is a block diagram of an adaptive recombinant system, accordingto some embodiments;

FIG. 17 is a block diagram of the adaptive recombinant system of FIG. 16in which the structural aspect is a fuzzy network, according to someembodiments;

FIG. 18 is a block diagram of the fuzzy network operators used by theadaptive recombinant system of FIG. 16, according to some embodiments;

FIGS. 19A and 19B are block diagrams of alternative topologies betweenfuzzy networks and adaptive processes, according to some embodiments;

FIGS. 20A and 20B are block diagrams of a process topic object and aprocess content object, respectively, according to some embodiments;

FIGS. 21A and 21B are block diagrams of alternative structures ofprocess activity objects, according to some embodiments;

FIGS. 22A and 22B are block diagrams of process activity networks,according to some embodiments;

FIGS. 23A and 23B are block diagrams of a process network, according tosome embodiments;

FIG. 24 is a flow diagram describing structural modification of theprocess network of FIGS. 23A and 23B, according to some embodiments;

FIG. 25 is a block diagram of a process network selection operation,according to some embodiments;

FIG. 26 is a block diagram of a process network syndication operation,according to some embodiments;

FIG. 27 is a block diagram of a process network resulting from acombination of process networks, according to some embodiments;

FIG. 28 is a block diagram of the adaptive system of FIG. 7 in which thestructural aspect is a process network, according to some embodiments;

FIG. 29 is a block diagram of the adaptive recombinant system of FIG. 16in which the structural aspect is a process network, according to someembodiments;

FIGS. 30A and 30B are block diagrams illustrating syndication andrecombination of process networks and process network subsets, accordingto some embodiments;

FIGS. 31A and 31B are block diagrams illustrating syndication andrecursive recombination of process networks and process network subsets,according to some embodiments;

FIG. 32 is a block diagram of the process network topologies, accordingto some embodiments;

FIG. 33 is a block diagram of extensions to the process networktopologies of FIG. 32, according to some embodiments;

FIG. 34 is a diagram of a process lifecycle framework, according to someembodiments;

FIG. 35 is a diagram of process functionality layers, according to someembodiments;

FIG. 36 is a diagram of a process lifecycle management framework,according to some embodiments;

FIG. 37 is a block diagram of an adaptive asset management system andprocess, according to some embodiments;

FIG. 38 is a block diagram of a real-time learning system interface,according to some embodiments;

FIG. 39 is a block diagram of an adaptive system to support aninnovation process, according to some embodiments;

FIG. 40 is a block diagram of a system and process for adaptivepublishing, according to some embodiments;

FIG. 41 is a block diagram of a system and process for adaptivecommerce, according to some embodiments;

FIG. 42 is a block diagram of a system and process for adaptive pricediscovery, according to some embodiments;

FIG. 43 is a block diagram of a system and process for adaptivecommercial solutions, according to some embodiments;

FIG. 44 is a block diagram of location aware collectively adaptivesystems, according to some embodiments;

FIG. 45 is a block diagram of a possible configuration of the locationaware collectively adaptive systems of FIG. 44, according to someembodiments;

FIG. 46 is a block diagram of an alternative configuration of thelocation aware collectively adaptive systems of FIG. 45, according tosome embodiments;

FIG. 47 is a block diagram of syndication and combination of contentnetworks within the structural aspect of the adaptive recombinant systemof FIG. 16, according to some embodiments;

FIG. 48 is a block diagram of syndication and combination of elements ofthe structural aspects and usage aspects across multiple instances ofadaptive systems of FIG. 7 within the adaptive recombinant system ofFIG. 16, according to some embodiments;

FIGS. 49A and 49B are block diagrams of recursive syndication andcombination of networks of the structural aspects of the adaptiverecombinant systems of FIG. 47 or 48 across organizations, according tosome embodiments;

FIG. 50 is a block diagram of an evolvable adaptive recombinant systemand process, according to some embodiments; and

FIG. 51 is a diagram of alternative computing topologies of adaptiverecombinant processes, according to some embodiments.

DETAILED DESCRIPTION

In the following description, numerous details are set forth to providean understanding of the present invention. However, it will beunderstood by those skilled in the art that the present invention may bepracticed without these details and that numerous variations ormodifications from the described embodiments may be possible.

In accordance with the embodiments described herein, a method and asystem for development, management and application of adaptive processesis disclosed.

Processes

FIGS. 1A, 1B, 2A, 2B and 3 describe prior art and definitions associatedwith processes.

FIG. 1A depicts a business enterprise 110 including a plurality ofprocesses, a specific example being “process 3” 105. A business mayinclude one or more processes. It is a typical practice to determine anumber of processes that can be effectively remembered and managed bypeople in the associated business—for example, seven processes (plus orminus two) is a commonly selected number of processes for anorganization. Although not explicitly shown in FIG. 1A, each process mayhave one or more linkages to another process. The linkages may denote aworkflow between the processes, or the linkage may denote an informationflow, or a linkage may denote both workflow and information flow.

As depicted in FIG. 1B, processes may extend across businesses orenterprises, or most broadly, organizations. For example, in FIG. 1B,“Process 8” 120 is shown extending across “Enterprise A” 110A and“Enterprise B” 110B.

It should be understood that, in general, multiple processes may extendacross multiple enterprises or organizations.

FIG. 2A illustrates that each process 125 may include one or moresub-processes. As in the case of processes, sub-processes may have oneor more directed linkages 132 to other sub-processes within the process,or to processes outside the process within which the sub-process exists.These external links may constitute inbound links 132 a or outboundlinks 132 d. There may exist a plurality of links between any twosub-processes, and the plurality of links may include inbound 132 b oroutbound links 132 c. Although not explicitly shown in FIG. 2A, eachsub-process may contain one or more other sub-processes, and thisrecursive decomposition of sub-processes can continue without limit. Itshould be noted, as defined herein, that the only essentialdistinguishing feature of a sub-process with regard to a process is thata sub-process is understood to be a subset of a process. Where the termsub-process is used herein, it is understood that the term process couldbe used without loss of generality.

FIG. 2B depicts a sub-process. A sub-process 135 is comprised of othersub-processes (not shown), and/or a series of activities, for example,“Activity 1” 140. These activities are conducted by process participants200. In a business setting, each activity typically represents a unit ofwork to be conducted in a prescribed manner by one or more participants200 in the process, and possibly according to a prescribed workflow.However, as defined herein, an activity may also simply constitute aprocess participant 200 action or behavior. For example, a processparticipant 200 for a sales process might be a prospective customer, anda behavior of the prospective customer may constitute an activity. Insuch cases a process participant, for example, a customer or prospectivecustomer, may not be aware that their behaviors or interactions with aprocess constitute conducting a formally defined activity, although fromthe perspective of another process participant or the process owner, theactivity may constitute a formally defined activity.

Participants in a process 200, or “process participants,” are defined asindividuals that perform some activity within a process, or otherwiseinteract with a process, or provide input to, or use the output from, aprocess or sub-process. For example, a process participant in a salesprocess may include sales people that perform selling activities, butmay also include customers or prospective customers that interact withthe sales process, including the review and consideration of, and/or thepurchasing of goods or services. Further, managers who rely on inputfrom, and/or provide guidance to, the sales process may be consideredprocess participants in the sales process. Further, specific actions orbehaviors of the customer or prospective customer may be defined asactivities corresponding to the process or sub-process.

Although more than one activity is depicted in FIG. 2B, it should beunderstood that a process or sub-process may include only a singleactivity.

Any two activities may be linked, which implies a temporal sequencing orworkflow, as for example the linkage 155 between “Activity 1” 140 and“Activity 2” 150. An activity may be cross-linked, back linked, orforward linked to more than one other activity. An activity may containconditional decisions that determine which forward links to otheractivities, such as depicted by links 155 a and 155 b, are selectedduring execution of the antecedent activity 150. Parallel activities mayexist as represented by “Activity 3” 161 and “Activity 4” 160. Inboundlinks 145 to activities of the sub-process 135 from other processes,sub-processes or activities may exist, as well as outbound links 165from activities of the sub-process 135 to other processes,sub-processes, or activities.

FIG. 3 illustrates a general approach to information and computinginfrastructure support for processes. The workflow of activities withina process or sub-process 168 may be managed by a computer-based workflowapplication 169 that enables the appropriate sequencing of workflow.Each activity, as for example “Activity 2” 170, may be supported byon-line content or computer applications 175. On-line content orcomputer applications 175 include pure content 180, a computerapplication 181, and a computer application that includes content 182.Information or content may be accessed by the sub-process 168 from eachof these sources, shown as content access 180 a, information access 181a, and information access 182 a.

For example, content 180 may be accessed 180 a (a content access 180 a)as an activity 170 is executed. Although multiple activities aredepicted in FIG. 3, a process or sub-process may include only oneactivity. The term “content” is defined broadly herein, to include text,graphics, video, audio, multi-media, computer programs or any othermeans of conveying relevant information. During execution of theactivity 170, an interactive computer application 181 may be accessed.During execution of the activity 170, information 181 a may be deliveredto, as well as received from the computer application 181. A computerapplication 182, accessible by process participants 200 during executionof the activity 170, and providing and receiving information 182 aduring execution of the activity 170, may also contain and managecontent such that content and computer applications and functions thatsupport an activity 170 may be combined within a computer application182. An unlimited number of content and computer applications maysupport a given activity, sub-process or process. A computer application182 may directly contain the functionality to manage workflow 169 forthe sub-process 168, or the workflow functionality may be provided by aseparate computer-based application.

Adaptive Processes

FIGS. 4A and 4B depict the application of adaptive recommendations tosupport a process or sub-process, according to some embodiments. In FIG.4A, an adaptive process 900 is depicted, which includes one or moreprocess participants 200, an adaptive instance of a process orsub-process 930 (hereinafter, adaptive process instance 930 or processinstance 930), and an adaptive computer-based application 925. In FIG.4B, the adaptive process 900 may include many of the features of theprior art process in FIG. 3. Thus, the adaptive process instance 930features the workflow application 169, if applicable, with multipleactivities 170, one or more of which may be linked. Further, theadaptive computer-based application 925 is depicted as part ofsupporting content and computer applications 175. FIG. 4A provides abroad overview of the adaptive process 900 while FIG. 4B includes manymore details.

One or more participants 200 in the adaptive process instance 930generate behaviors associated with their participation in the processinstance 930. The participation in the process instance 930 may includeinteractions with computer-based systems 181 and content 180, such ascontent access 180 a and information access 181 a, but may also includebehaviors not directly associated with interactions with computer-basedsystems or content.

Process participants 200 may be identified by the adaptivecomputer-based application 925 through any means of computer-basedidentification, including, but not limited to, sign-in protocols orbio-metric-based means of identification; or through indirect meansbased on identification inferences derived from selective process usagebehaviors 920.

The adaptive process 900 includes an adaptive computer-based application925, which includes one or more system elements or objects, each elementor object being executable software and/or content that is meant fordirect human access. The adaptive computer-based application 925 tracksand stores selective process participant behaviors 920 associated with aprocess instance 930. It should be understood that the tracking andstoring of selective behaviors by the adaptive computer-basedapplication 925 may also be associated with one or more other processes,sub-processes, and activities other than the process instance 930,though this is not explicitly depicted in FIGS. 4A and 4B. In additionto the direct tracking and storing of selective process usage behaviors,the adaptive computer-based application 925 may also indirectly acquireselective behaviors associated with process usage through one or moreother computer-based applications that track and store selective processparticipant behaviors.

FIGS. 4A and 4B also depict adaptive recommendations 910 being generatedand delivered by the adaptive computer-based application 925 to processparticipants 200. The adaptive recommendations 910 are shown beingdelivered to one or more process participants 200 engaged in “Activity2” 170 of the adaptive process instance 930 in FIG. 4B. It should beunderstood that the adaptive recommendations 910 may be delivered toprocess participants 200 during any activity or any other point duringparticipation in a process or sub-process.

The adaptive recommendations 910 delivered by the adaptivecomputer-based application 925 are informational or computing elementsor subsets of the adaptive computer-based application 925, and may takethe form of text, graphics, Web sites, audio, video, interactivecontent, other computer applications, or embody any other type or itemof information. These recommendations are generated to facilitateparticipation in, or use of, an associated process, sub-process, oractivity. The recommendations are derived by combining the context ofwhat the process participant is currently doing and the inferredpreferences or interests of the process participant based, at least inpart, on the behaviors of one or more process participants, to generaterecommendations. As the process, sub-process or activity is executedmore often by the one or more process participants, the recommendationsadapt to become increasingly effective. Hence, the adaptive process 900itself can adapt over time to become increasingly effective.

Furthermore, the adaptive recommendations 910 may be applied toautomatically or semi-automatically self-modify 905 the structure,elements, objects, content, information, or software of a subset 1632 ofthe adaptive computer-based application 925, including representationsof process workflow. (The terms “semi-automatic” or“semi-automatically,” as used herein, are defined to mean that thedescribed activity is conducted through a combination of one or moreautomatic computer-based operations and one or more direct humaninterventions.) For example, the elements, objects, or items of contentof the adaptive computer-based application 925, or the relationshipsamong elements, objects, or items of content associated with theadaptive computer-based application 925 may be modified 905 based oninferred preferences or interests of one or more process participants.These modifications may be based solely on inferred preferences orinterests of the one or more process participants 200 derived fromprocess usage behaviors, or the modifications may be based on inferencesof preferences or interests of process participants 200 from processusage behaviors integrated with inferences based on the intrinsiccharacteristics of elements, objects or items of content of the adaptivecomputer-based application 925. These intrinsic characteristics mayinclude patterns of text, images, audio, or any other information-basedpatterns.

For example, inferences of subject matter based on the statisticalpatterns of words or phrases in a text-based item of content associatedwith the adaptive computer-based application 925 may be integrated withinferences derived from the process usage behaviors of one or moreprocess participants to generate adaptive recommendations 910 that maybe applied to deliver to participants in the process, or may be appliedto modify 905 the structure of the adaptive computer-based application925, including the elements, objects, or items of content of theadaptive computer-based application 925, or the relationships amongelements, objects, or items of content associated with the adaptivecomputer-based application 925.

Structural modifications 905 applied to the adaptive computer-basedapplication 925 enables the structure to adapt to process participantpreferences, interests, or requirements over time by embeddinginferences on these preferences, interests or requirements directlywithin the structure of the adaptive computer-based application 925 on apersistent basis.

Adaptive recommendations generated by the adaptive computer-basedapplication 925 may be applied to modify the structure, includingobjects and items of content, of other computer-based systems 175,including the computer-based workflow application 169, supporting, oraccessible by, participants in the process instance 930. For example, asystem that manages workflow 169 may be modified through application ofadaptive recommendations generated by the adaptive computer-basedapplication 925, potentially altering activity sequencing or otherworkflow aspects for one or more process participants associated withthe adaptive process instance 930.

In addition to adaptive recommendations 910 being delivered to processparticipants 200, process participants 200 may also access or interact915 with adaptive computer-based application 925 in other ways. Theaccess of, or interaction with, 915 the adaptive computer-basedapplication 925 by process participants 200 is analogous to theinteractions 182 a with computer application 182 of FIG. 3. However, adistinguishing feature of adaptive process 900 is that the access orinteraction 915 of the adaptive computer-based application 925 byprocess participants 200 may include elements 1632 of the adaptivecomputer-based application 925 that have been adaptively self-modified905 by the adaptive computer-based application 925.

FIG. 4C depicts an extension of the adaptive process 900 of FIG. 4A inwhich the adaptive recombinant function 850 is combined with theadaptive computer-based application 925 to form an adaptive recombinantcomputer-based application 925R. The adaptive recombinant computer-basedapplication 925R enables the management of multiple computer-basedrepresentations of adaptive process or sub-process instances 930, whereeach process or sub-process representation may be in whole or in part.Further, the adaptive recombinant computer-based application 925Renables the management of multiple information structures associatedwith a specific process instance 930. The management of therepresentations of process or sub-process instances 930 and/or multipleinformation structures thereof, may include the distribution andcombination of the representations of process or sub-process instances930 and/or other information structures, within or across computingsystems and/or organizations. These capabilities enable the adaptiverecombinant process 901.

For some process applications described herein, adaptive process 900 issufficient to implement the application. Other process applicationsdescribed herein utilize the additional adaptive recombinantcapabilities 850 provided by the adaptive recombinant process 901 forfull implementation. Notwithstanding that the term “adaptive recombinantprocesses” is the general term used herein to describe the presentinvention, it should be understood that in some process applicationareas, the additional adaptive recombinant capabilities 850 of theadaptive recombinant process 901 (that are extensions to the adaptiveprocess capabilities of the adaptive process 900) are not necessary forimplementation.

Process Participant Behavior Categories

In Table 1, several different process participant behaviors 920, whichmay also be described as process “usage” behaviors without loss ofgenerality, are identified by the adaptive computer-based application925 and categorized. The usage behaviors 920 may be associated with theentire community of process participants, one or more sub-communities,or with individual process participants or users associated with thesub-process instance 930.

TABLE 1 Usage behavior categories and usage behaviors usage behaviorcategory usage behavior examples navigation and access activity, contentand computer application accesses, including buying/selling paths ofaccesses or click streams subscription and personal or communitysubscriptions to self-profiling process topical areas interest andpreference self-profiling affiliation self-profiling (e.g., jobfunction) collaborative referral to others discussion forum activitydirect communications (voice call, messaging) content contributions orstructural alterations reference personal or community storage andtagging personal or community organizing of stored or tagged informationdirect feedback user ratings of activities, content, computerapplications and automatic recommendations user comments attentiondirection of gaze brain patterns physical location current locationlocation over time relative location to users/object references

A first category of process usage behaviors 920 is known as systemnavigation and access behaviors. System navigation and access behaviorsinclude usage behaviors 920 such as accesses to, and interactions withonline computer applications and content such as documents, Web pages,images, videos, audio, multi-media, interactive content, interactivecomputer applications, e-commerce applications, or any other type ofinformation item or system “object.” These process usage behaviors maybe conducted through use of a keyboard, a mouse, oral commands, or usingany other input device. Usage behaviors 920 in the system navigation andaccess behaviors category may include, but are not limited to, theviewing or reading of displayed information, typing written information,interacting with online objects orally, or combinations of these formsof interactions with computer-based applications.

System navigation and access behaviors may also include executingtransactions, including commercial transactions, such as the buying orselling of merchandise, services, or financial instruments. Systemnavigation and access behaviors may include not only individual accessesand interactions, but the capture and categorization of sequences ofinformation or system object accesses and interactions over time.

A second category of usage behaviors 920 is known as subscription andself-profiling behaviors. Subscriptions may be associated with specifictopical areas or other elements of the adaptive computer-basedapplication 925, or may be associated with any other subset of theadaptive computer-based application 925. Subscriptions may thus indicatethe intensity of interest with regard to elements of the adaptivecomputer-based application 925. The delivery of information to fulfillsubscriptions may occur online, such as through electronic mail (email),on-line newsletters, XML feeds, etc., or through physical delivery ofmedia.

Self-profiling refers to other direct, persistent (unless explicitlychanged by the user) indications explicitly designated by the one ormore process participants regarding their preferences and interests, orother meaningful attributes. A process participant 200 may explicitlyidentify interests or affiliations, such as job function, profession, ororganization, and preferences, such as representative skill level (e.g.,novice, business user, advanced). Self-profiling enables the adaptivecomputer-based application 925 to infer explicit preferences of theprocess participant. For example, a self-profile may contain informationon skill levels or relative proficiency in a subject area,organizational affiliation, or a position held in an organization. Aprocess participant 200 that is in the role, or potential role, of asupplier or customer may provide relevant context for effective adaptivee-commerce applications through self-profiling. For example, a potentialsupplier may include information on products or services offered in hisor her profile. Self-profiling information may be used to inferpreferences and interests with regard to system use and associatedtopical areas, and with regard to degree of affinity with other processparticipant community subsets. A process participant may identifypreferred methods of information receipt or learning style, such asvisual or audio, as well as relative interest levels in othercommunities.

A third category of usage behaviors 920 is known as collaborativebehaviors. Collaborative behaviors are interactions among the one ormore process participants. Collaborative behaviors may thus provideinformation on areas of interest and intensity of interest. Interactionsincluding online referrals of elements or subsets of the adaptivecomputer-based application 925, such as through email, whether to otherprocess participants or to non-process participants, are types ofcollaborative behaviors obtained by the adaptive computer-basedapplication 925.

Other examples of collaborative behaviors include, but are not limitedto, online discussion forum activity, contributions of content or othertypes of objects to the adaptive computer-based application 925, or anyother alterations of the elements, objects or relationships among theelements and objects of adaptive computer-based application 925.Collaborative behaviors may also include general user-to-usercommunications, whether synchronous or asynchronous, such as email,instant messaging, interactive audio communications, and discussionforums, as well as other user-to-user communications that can be trackedby the adaptive computer-based application 925.

A fourth category of process usage behaviors 920 is known as referencebehaviors. Reference behaviors refer to the saving or tagging ofspecific elements or objects of the adaptive computer-based application925 for recollection or retrieval at a subsequent time. The saved ortagged elements or objects may be organized in a manner customizable byprocess participants. The referenced elements or objects, as well as themanner in which they are organized by the one or more processparticipants, may provide information on inferred interests of the oneor more process participants and the associated intensity of theinterests.

A fifth category of process usage behaviors 920 is known as directfeedback behaviors. Direct feedback behaviors include ratings or otherindications of perceived quality by individuals of specific elements orobjects of the adaptive computer-based application 925, or theattributes associated with the corresponding elements or objects. Thedirect feedback behaviors may therefore reveal the explicit preferencesof the process participant. In the adaptive computer-based application925, the adaptive recommendations 910 may be rated by processparticipants 200. This enables a direct, adaptive feedback loop, basedon explicit preferences specified by the process participant. Directfeedback also includes user-written comments and narratives associatedwith elements or objects of the computer-based system 925.

A sixth category of process usage behaviors is known as attentionbehaviors. These behaviors are associated with the focus of attention ofprocess participants and/or the intensity of the intention. For example,the direction of the visual gaze of one or more process participants maybe determined. This behavior can inform inferences associated withpreferences or interests even when no physical interaction with theadaptive computer-based application 925 is occurring. Even more directassessment of the level of attention may be conducted through access tothe brain patterns or signals associated with the one or more processparticipants. Such patterns of brain functions during participation in aprocess can inform inferences on the preferences or interests of processparticipants, and the intensity of the preferences or interests. Thebrain patterns assessed may include MRI images, brain wave patterns,relative oxygen use, or relative blood flow by one or more regions ofthe brain.

Attention behaviors may include any other type of physiological responseof a process participant 200 that may be relevant for making preferenceor interest inferences, independently, or collectively with the otherusage behavior categories. Other physiological responses may include,but are not limited to, utterances, gestures, movements, or bodyposition. Attention behaviors may also include other physiologicalresponses such as breathing rate, blood pressure, or galvanic response.

A seventh category of process usage behaviors is known as physicallocation behaviors. Physical location behaviors identify physicallocation and mobility behaviors of process participants. The location ofa process participant may be inferred from, for example, informationassociated with a Global Positioning System or any other positionally orlocationally aware system or device. The physical location of physicalobjects referenced by elements or objects of adaptive computer-basedapplication 925 may be stored for future reference. Proximity of aprocess participant to a second process participant, or to physicalobjects referenced by elements or objects of the computer-basedapplication, may be inferred. The length of time, or duration, at whichone or more process participants reside in a particular location may beused to infer intensity of interests associated with the particularlocation, or associated with objects that have a relationship to thephysical location. Derivative mobility inferences may be made fromlocation and time data, such as the direction of the processparticipant, the speed between locations or the current speed, thelikely mode of transportation used, and the like. These derivativemobility inferences may be made in conjunction with geographiccontextual information or systems, such as through interaction withdigital maps or map-based computer systems.

In addition to the usage behavior categories depicted in Table 1, usagebehaviors may be categorized over time and across user behavioralcategories. Temporal patterns may be associated with each of the usagebehavioral categories. Temporal patterns associated with each of thecategories may be tracked and stored by the adaptive computer-basedapplication 925. The temporal patterns may include historical patterns,including how recently an element, object or item of content associatedwith adaptive computer-based application 925. For example, more recentbehaviors may be inferred to indicate more intense current interest thanless recent behaviors.

Another temporal pattern that may be tracked and contribute topreference inferences that are derived is the duration associated withthe access or interaction with the elements, objects or items of contentof the adaptive computer-based application 925, or the user's physicalproximity to physical objects referenced by system objects of theadaptive computer-based application 925, or the user's physicalproximity to other process participants. For example, longer durationsmay generally be inferred to indicate greater interest than shortdurations. In addition, trends over time of the behavior patterns may becaptured to enable more effective inference of interests and relevancy.Since adaptive recommendations 910 may include one or more elements,objects or items of content of the adaptive computer-based application925, the usage pattern types and preference inferencing may also applyto interactions of the one or more process participants with theadaptive recommendations 910 themselves.

Process Participant Behavior and Usage Framework

FIG. 5 depicts a usage framework 1000 for performing preferenceinferencing of tracked or monitored usage behaviors 920 associated witha process or sub-process instance 930 by the adaptive computer-basedapplication 925. The usage framework 1000 summarizes the manner in whichprocess usage patterns are managed within the adaptive computer-basedapplication 925. Usage behavioral patterns associated with an entirecommunity, affinity group, or segment of process participants 1002 arecaptured by the adaptive computer-based application 925. In anothercase, usage patterns specific to an individual, shown in FIG. 5 asindividual usage patterns 1004, are captured by the adaptivecomputer-based application 925. Various sub-communities of usageassociated with process participants may also be defined, as for examplesub-community A usage patterns 1006, sub-community B usage patterns1008, and sub-community C usage patterns 1010.

Memberships in the communities are not necessarily mutually exclusive,as depicted by the overlaps of the sub-community A usage patterns 1006,sub-community B usage patterns 1008, and sub-community C usage patterns1010 (as well as and the individual usage patterns 1004) in the usageframework 1000. Recall that a community may include a single processparticipant or multiple process participants. Sub-communities maylikewise include one or more process participants. Thus, the individualusage patterns 1004 in FIG. 5 may also be described as representing theprocess usage patterns of a community or a sub-community. For theadaptive computer-based application 925, usage behavior patterns may besegmented among communities and individuals so as to effectively enableadaptive recommendations 910, 905 for each sub-community or individual.

The communities identified by the adaptive computer-based application925 may be determined through self-selection, through explicitdesignation by other process participants or external administrators(e.g., designation of certain process participants as “experts”), orthrough automatic determination by the adaptive computer-basedapplication 925. The communities themselves may have relationshipsbetween each other, of multiple types and values. In addition, acommunity may be composed not of human users, or solely of human users,but instead may include one or more other computer-based systems, whichmay have reason to interact with the adaptive computer-based application925. Or, such computer-based systems may provide an input into theadaptive computer-based application 925, such as by being the outputfrom a search engine. The interacting computer-based system may beanother instance of the adaptive computer-based application 925.

The usage behaviors 920 included in Table 1 may be categorized by theadaptive computer-based application 925 according to the usage framework1000 of FIG. 5. For example, categories of usage behavior may becaptured and categorized according to the entire community usagepatterns 1002, sub-community usage patterns 1006, and individual usagepatterns 1004. The corresponding usage behavior information may be usedto infer preferences and interests at each of the user levels.

Multiple usage behavior categories shown in Table 1 may be used by theadaptive computer-based application 925 to make reliable inferences ofthe preferences of a process participant with regard to elements,objects, or items of content associated with the adaptive computer-basedapplication 925. There are likely to be different preference inferencingresults for different process participants. In addition, preferenceinferencing may be different with regard to optimizing the delivery ofadaptive recommendations 910 to process participants than the preferenceinferencing optimized for modifying the structure 905 of the adaptivecomputer-based application 925, as modifications to the structure arelikely to be persistent and affect many process participants.

As an example, simply using the sequences of content accesses as thesole relevant usage behavior on which to base updates to the structurewill generally yield unsatisfactory results. This is because thestructure itself, through navigational proximity, will create a tendencytoward certain navigational access sequence biases. Using just object orcontent access sequence patterns as the basis for updates to thestructural will therefore tend to reinforce the pre-existing structureof the adaptive computer-based application 925, which may limit theadaptiveness of the adaptive computer-based application 925.

By introducing different or additional behavioral characteristics, suchas the duration of access of an item of content, on which to baseupdates to the structure of adaptive computer-based application 925, amore adaptive process is enabled. For example, duration of access willgenerally be much less correlated with navigational proximity thanaccess sequences will be, and therefore provide a better indicator oftrue user preferences. Therefore, combining access sequences and accessduration will generally provide better inferences and associated systemstructural updates than using either usage behavior alone. Effectivelyutilizing additional usage behaviors as described above will generallyenable increasingly effective system structural updating. In addition,the adaptive computer-based application 925 may employ user affinitygroups to enable even more effective system structural updating than areavailable merely by applying either individual (personal) usagebehaviors or entire community usage behaviors.

Furthermore, relying on only one or a limited set of usage behavioralcues and signals may more easily enable potential “spoofing” or “gaming”of the computer-based application 925. “Spoofing” or “gaming” theadaptive computer-based application 925 refers to conducting consciouslyinsincere or otherwise intentional usage behaviors 920, so as toinfluence the adaptive recommendations 910 or adaptive modifications 905to the intrinsic elements and structure of the adaptive computer-basedapplication 925. Utilizing broader sets of system usage behavioral cuesand signals may lessen the effects of spoofing or gaming. One or morealgorithms may be employed by computer-based application 925 to detectsuch contrived usage behaviors, and when detected, such behaviors may becompensated for by the preference and interest inferencing algorithms ofcomputer-based application 925.

In some embodiments, the computer-based application 925 may provideprocess participants 200 with a means to limit the tracking, storing, orapplication of their usage behaviors 920. A variety of limitationvariables may be selected by the process participant 200. For example, aprocess participant 200 may be able to limit usage behavior tracking,storing, or application by usage behavior category described in Table 1.Alternatively, or in addition, the selected limitation may be specifiedto apply only to particular user communities or individual processparticipants 200. For example, a process participant 200 may restrictthe application of the full set of her process usage behaviors 920 topreference or interest inferences by adaptive computer-based application925 for application to only herself, and make a subset of processbehaviors 920 available for application to process participants onlywithin her workgroup, but allow none of her process usage behaviors tobe applied by computer-based application 925 in making inferences ofpreferences or interests for other process participants.

Process Participant Communities

As described above, a process participant associated with an adaptiveprocess instance 930 may be a member of one or more communities ofinterest, or affinity groups, with a potentially varying degree ofaffinity associated with the respective communities. These affinitiesmay change over time as interests of the user 200 and communities evolveover time. The affinities or relationships among process participantsand communities may be categorized into specific types. An identifiedprocess participant 200 may be considered a member of a specialsub-community containing only one member, the member being theidentified process participant. A process participant can therefore bethought of as just a specific case of the more general notion of processparticipant or user segments, communities, or affinity groups.

FIG. 6 illustrates the affinities among user communities and how theseaffinities may automatically or semi-automatically be updated by theadaptive computer-based application 925 based on user preferences whichare derived from process participant behaviors 920. An entire community1050 is depicted in FIG. 6. The community may extend acrossorganizational, functional, or process boundaries. The entire community1050 extends across process A 1060 and process B 1061. The entirecommunity 1050 includes sub-community A 1064, sub-community B 1062,sub-community C 1069, sub-community D 1065, and sub-community E 1070. Aprocess participant 1063 who is not part of the entire community 1050 isalso featured in FIG. 6.

Sub-community B 1062 is a community that has many relationships oraffinities to other communities. These relationships may be of differenttypes and differing degrees of relevance or affinity. For example, afirst relationship 1066 between sub-community B 1062 and sub-community D1065 may be of one type, and a second relationship 1067 may be of asecond type. (In FIG. 6, the first relationship 1066 is depicted using adouble-pointing arrow, while the second relationship 1067 is depictedusing a unidirectional arrow.) The relationships 1066 and 1067 may bedirectionally distinct, and may have an indicator of relationship oraffinity associated with each distinct direction of affinity orrelationship. For example, the first relationship 1066 has a numericalvalue 1068, or relationship value, of “0.8.” The relationship value 1068thus describes the first relationship 1066 between sub-community B 1062and sub-community D 1065 as having a value of 0.8.

The relationship value may be scaled as in FIG. 6 (e.g., between 0 and1), or may be scaled according to another interval. The relationshipvalues may also be bounded or unbounded, or they may be symbolicallyrepresented (e.g., high, medium, low).

The process participant 1063, which could be considered a processparticipant community including a single member, may also have a numberof relationships to other communities, where these relationships are ofdifferent types, directions and relevance. From the perspective of theprocess participant 1063, these relationship types may take manydifferent forms. Some relationships may be automatically formed by theadaptive computer-based application 925, for example, based on interestsor geographic location or similar traffic/usage patterns. Thus, forexample the entire community 1050 may include process participants in aparticular city. Some relationships may be context-relative. Forexample, a community to which the process participant 1063 has arelationship could be associated with a certain process, and anothercommunity could be related to another process. Thus, sub-community E1070 may be the process participants associated with a productdevelopment business to which the process participant 1063 has arelationship 1071; sub-community B 1062 may be the members of across-business innovation process to which the user 1063 has arelationship 1073; sub-community D 1065 may be experts in a specificdomain of product development to which the process participant 1063 hasa relationship 1072. The generation of new communities which include theprocess participant 1063 may be based on the inferred interests of theprocess participant 1063 or other process participants within the entirecommunity 1050.

Membership of communities may overlap, as indicated by sub-communities A1064 and C 1069. The overlap may result when one community is wholly asubset of another community, such as between the entire community 1050and sub-community B 1062. More generally, a community overlap will occurwhenever two or more communities contain at least one processparticipant or user in common. Such community subsets may be formedautomatically by the adaptive process 900, based on preferenceinferencing from process participant behaviors 920. For example, asubset of a community may be formed based on an inference of increasedinterest or demand of particular content or expertise of an associatedcommunity. The adaptive computer-based application 925 is also capableof inferring that a new community is appropriate. The adaptivecomputer-based application 925 of the adaptive process 900 will thuscreate the new community automatically.

For each process participant, whether residing within, say,sub-community A 1064, or residing outside the community 1050, such asthe process participant 1063, the relationships (such as arrows 1066 or1067), affinities, or “relationship values” (such as numerical indicator1068), and directions (of arrows) are unique. Accordingly, somerelationships (and specific types of relationships) between communitiesmay be unique to each process participant. Other relationships,affinities, values, and directions may have more general aspects orreferences that are shared among many process participants, or among allprocess participants of the adaptive process 900. A distinct and uniquemapping of relationships between process participants, such as isillustrated in FIG. 6, could thus be produced for each processparticipant by the adaptive computer-based application 925.

The adaptive computer-based application 925 may automatically generatecommunities, or affinity groups, based on process participant behaviors920 and associated preference inferences. In addition, communities maybe identified by process participants, such as administrators of theprocess or sub-process instance 930. Thus, the adaptive computer-basedapplication 925 utilizes automatically generated and manually generatedcommunities in generating adaptive recommendations 910, 905.

The communities, affinity groups, or user segments aid the adaptivecomputer-based application 925 in matching interests optimally,developing learning groups, prototyping process designs beforeadaptation, and many other uses. For example, some process participantsthat use or interact with the adaptive computer-based application 925may receive a preview of a new adaptation of a process for testing andfine-tuning, prior to other process participants receiving this change.

The process participants or communities may be explicitly represented aselements or objects within the adaptive computer-based application 925.This feature enhances the extensibility and adaptability of the adaptiveprocess 900.

Adaptive System

FIG. 7 depicts a possible configuration of the adaptive computer-basedapplication 925, as part of the adaptive process 900 of FIGS. 4A and 4B.The adaptive computer-based application 925 includes, at least in part,an adaptive system 100 (shaded for convenience of identification),according to some embodiments. The adaptive system 100 includes threeaspects: 1) a structural aspect 210, a usage aspect 220, and a contentaspect 230. One or more process participants 200 (who may also be termed“users” of the adaptive process 900) interact with, or are monitored by,the adaptive system 100, which tracks selected behaviors 920 of theprocess participants, which are in turn selectively stored and processedby the usage aspect 220. An adaptive recommendations function 240generates adaptive recommendations based on inputs from the usage aspect220, and, optionally, based on the structural aspect 210 and/or thecontent aspect 230. The adaptive recommendations function 240 determinesinferred interests of process participants 200, and generates adaptiverecommendations 250 that may be delivered 910 to process participants200 or may be delivered 265 to non-process participants 260. Theadaptive recommendations function 240 may also apply adaptiverecommendations to modify 905 the structural aspect 210 or to modify 935the content aspect 230.

In some embodiments, the adaptive process 900 utilizes the methods andsystems of adaptive fuzzy network and process models, as defined in U.S.Pat. No. 6,795,826, entitled “Fuzzy Content Network Management andAccess,” and PCT Patent Application No. PCT/US04/37176, entitled“Adaptive Recombinant Systems,” filed on Nov. 4, 2004, which are herebyincorporated by reference as if set forth in their entirety.

FIG. 8 contrasts the non-adaptive computer-based application 182 (FIG.3) with the adaptive computer-based application 925 (FIGS. 4A and 4B).In FIG. 8, an adaptive computer-based application 925 includes thenon-adaptive computer-based application 182 (FIG. 3), plus otherfeatures of the adaptive system 100 (FIG. 7). The non-adaptivecomputer-based application 182 includes at least a structural aspect anda content aspect, but does not include a usage aspect 220 and anadaptive recommendations function 240, and therefore cannot generate andapply 910, 905, 935 adaptive recommendations. The structural aspect orcontent aspect of the non-adaptive computer-based application 182 may beintegrated with a usage aspect 220 and an adaptive recommendationfunction 240 to create the adaptive system 100 (FIG. 7), and hence, theadaptive computer-based application 925. This integration may be throughintegration of the associated software functions of the structuralaspect 210 and the content aspect 230 of the non-adaptive computer-basedapplication 182 with a usage aspect 220 and an adaptive recommendationfunction 240. Or, the integration may be effected through transmissionof elements of the structural aspect 210 and the content aspect 230 ofthe non-adaptive computer-based application 182 with a second systemthat contains usage aspect 220 and an adaptive recommendation function240.

As used herein, one or more process participants 200 may be a singleuser or multiple users of the adaptive computer-based application 925.As shown in FIG. 8, the one or more process participants or users 200may receive 910 the adaptive recommendations 250. Individuals notparticipating in the process 260 of the adaptive system 100 may alsoreceive 265 adaptive recommendations 250 from the adaptive system 100.

The process participant or user 200 may be a human entity, a computersystem, or a second adaptive system (distinct from the adaptive system100) that interacts with, or otherwise uses the adaptive computer-basedapplication 925 and the associated adaptive system 100. The one or moreusers 200 may include non-human users of the adaptive system 100. Inparticular, one or more other adaptive systems may serve as virtualsystem “users.” These other adaptive systems may operate in accordancewith the architecture of the adaptive system 100. Thus, multipleadaptive systems may be mutual users for one another. These adaptivesystems may each support the same process, or each system 100 may eachsupport different processes.

It should be understood that the structural aspect 210, the contentaspect 230, the usage aspect 220, and the recommendations function 240of the adaptive system 100, and elements of each, may be containedwithin one computer, or distributed among multiple computers.Furthermore, one or more non-adaptive computer-based applications 182may be modified to comprise one or more adaptive systems 100 byintegrating the usage aspect 220 and the recommendations function 240with the one or more non-adaptive computer-based applications 182.

The term “computer system” or the term “system,” without furtherqualification, as used herein, will be understood to mean either anon-adaptive or an adaptive system. Likewise, the terms “systemstructure” or “system content,” as used herein, will be understood torefer to the structural aspect 210 and the content aspect 230,respectively, whether associated with the non-adaptive system 182 or theadaptive computer-based application 925, and associated adaptive system100. The term “system structural subset” or “structural subset,” as usedherein, will be understood to mean a portion or subset of the structuralaspect 210 of a system.

Structural Aspect

The structural aspect 210 of the adaptive system 100 is depicted in theblock diagram of FIG. 9A. The structural aspect 210 denotes a collectionof system objects 212 that are part of the adaptive system 100, as wellas the relationships among the objects 214. The relationships amongobjects 214 may be persistent across user sessions, or may be transientin nature. The objects 212 may include or reference items of content,such as text, graphics, audio, video, interactive content, or embody anyother type or item of information. The objects 212 may also includereferences to content, such as pointers. Computer applications,executable code, or references to computer applications may also bestored or referenced as objects 212 in the adaptive system 100. Thecontent of the objects 212 is known herein as information 232. Theinformation 232, though part of the object 214, is also considered partof the content aspect 230, as depicted in FIG. 9B, and as describedbelow.

The objects 212 may be managed in a relational database, or may bemaintained in structures such as flat files, linked lists, invertedlists, hypertext networks, or object-oriented databases. The objects 212may include meta-information 234 associated with the information 232contained within, or referenced by the objects 212.

As an example, in some embodiments, the World-wide Web may be considereda structural aspect, where web pages constitute the objects of thestructural aspect and links between web pages constitute therelationships among the objects. Alternatively, or in addition, in someembodiments, the structural aspect may feature objects associated withan object-oriented programming language, and the relationships betweenthe objects associated with the protocols and methods associated withinteraction and communication among the objects in accordance with theobject-oriented programming language.

The one or more users 200 of the adaptive system 100 may be explicitlyrepresented as objects 212 within the system 100, thereby becomingdirectly incorporated within the structural aspect 210. Therelationships among objects 214 may be arranged in a hierarchicalstructure, a relational structure (e.g. according to a relationaldatabase structure), or according to a network structure.

Content Aspect

The content aspect 230 of the adaptive system 100 is depicted in theblock diagram of FIG. 9B. The content aspect 230 denotes the information232 contained in, or referenced by the objects 212 that are part of thestructural aspect 210. The content aspect 230 of the objects 212 mayinclude text, graphics, audio, video, and interactive forms of content,such as applets, tutorials, courses, demonstrations, modules, orsections of executable code or computer programs. The one or more users200 interact with the content aspect 230.

The adaptive system 100 may enable an item of information 232 to bedecomposed into other items of information 232. For example, a textdocument could be decomposed into sections, each of which could becomeseparate items of information 232. Further, these items of informationcould then become an object 212; that is, an explicit element of thestructural aspect 210. The decomposition process may also generateappropriate relationships 214 among the decomposed objects, which alsobecome explicit elements of the structural aspect 210. The recursivedecomposition of information 232 into other information 232 andassociated objects 212 and corresponding relationships among the objects214 may continue without limit.

The content aspect 230 may be updated or modified 935 (FIG. 7) by theadaptive recommendations function 240 based, at least in part, on theusage aspect 220, including usage behavior metrics. To achieve this, theadaptive system 100 may employ the usage aspect, or elements of theusage aspect, of other systems. Such systems may include, but are notlimited to, other computer systems, other networks, such as the WorldWide Web, multiple computers within an organization, other adaptivesystems, or other adaptive recombinant systems. In this manner, thecontent aspect 230 benefits from usage occurring in other environments,including other process environments.

Usage Aspect

The usage aspect 220 of the adaptive system 100 is depicted in the blockdiagram of FIG. 9C. Recall from FIG. 7 that the usage aspect 220 tracksor monitor usage behaviors 920 of process participants 200. The usageaspect 220 denotes captured usage information 202, further identified asusage behaviors 270, and usage behavior pre-processing 204. The usageaspect 220 thus reflects the tracking, storing, categorization, andclustering of the use and associated usage behaviors 920 of the one ormore users or process participants 200 interacting with the adaptivesystem 100.

The captured usage information 202, known also as system usage or systemuse 202, includes any interaction by the one or more processparticipants or users 200 with the system, or monitored behavior by theone or more users 200. The adaptive system 100 may track and store userkey strokes and mouse clicks, for example, as well as the time period inwhich these interactions occurred (e.g., timestamps), as captured usageinformation 202. From this captured usage information 202, the adaptivesystem 100 identifies usage behaviors 270 of the one or more processparticipants 200 (e.g., web page access or physical location changes ofthe process participant). Finally, the usage aspect 220 includesusage-behavior pre-processing, in which usage behavior categories 246,usage behavior clusters 247, and usage behavioral patterns 248 areformulated for subsequent processing of the usage behaviors 270 by theadaptive system 100. Some usage behaviors 270 identified by the adaptivesystem 100, as well as usage behavior categories 246 designated by theadaptive system 100, are listed in Table 1, above, and are described inmore detail below.

The usage behavior categories 246, usage behaviors clusters 247, andusage behavior patterns 248 may be interpreted with respect to a singleuser 200, or to multiple users 200, in which the multiple users may bedescribed herein as a community, an affinity group, or a user segment.These terms are used interchangeably herein. A community is a collectionof one or more users, and may include what is commonly referred to as a“community of interest.” A sub-community is also a collection of one ormore users, in which members of the sub-community include a portion ofthe users in a previously defined community. Communities, affinitygroups, and user segments are described in more detail, below.

Usage behavior categories 246 include types of usage behaviors 270, suchas accesses, referrals to other users, collaboration with other users,and so on. These categories and more are included in Table 1, above.Usage behavior clusters 247 are groupings of one or more usage behaviors270, either within a particular usage behavior category 246 or acrosstwo or more usage categories. The usage behavior pre-processing 204 mayalso determine new “clusterings” of user behaviors 270 in previouslyundefined usage behavior categories 246, across categories, or among newcommunities. Usage behavior patterns 248, also known as “usagebehavioral patterns” or “behavioral patterns,” are also groupings ofusage behaviors 270 across usage behavior categories 246. Usage behaviorpatterns 248 are generated from one or more filtered clusters ofcaptured usage information 202.

The usage behavior patterns 248 may also capture and organize capturedusage information 202 to retain temporal information associated withusage behaviors 270. Such temporal information may include the durationor timing of the usage behaviors 270, such as those associated withreading or writing of written or graphical material, oralcommunications, including listening and talking, or physical location ofthe process participant 200. The usage behavioral patterns 248 mayinclude segmentations and categorizations of usage behaviors 270corresponding to a single user of the one or more users 200 or accordingto multiple users 200 (e.g., communities or affinity groups). Thecommunities or affinity groups may be previously established, or may begenerated during usage behavior pre-processing 204 based on inferredusage behavior affinities or clustering. Usage behaviors 270 may also bederived from the use or explicit preferences 252 associated with otheradaptive or non-adaptive systems.

Adaptive Recommendations Function

Returning to FIG. 7, the adaptive system 100 includes an adaptiverecommendations function 240, which interacts with the structural aspect210, the usage aspect 220, and the content aspect 230. The adaptiverecommendations function 240 generates adaptive recommendations 250based on the application of the usage aspect 220, and, optionally, thestructural aspect 210 and/or the content aspect 230. The adaptiverecommendations function 240 may also optionally apply other contextualinformation, rules, or algorithms through the application of othercomputer-based functions residing within adaptive system 100, or throughaccess to, or interaction with, other computer-based functions residingoutside of adaptive system 100.

The term “recommendations” associated with the adaptive recommendationsfunction 240 is used broadly in the adaptive system 100. The adaptiverecommendations 250 generated by recommendations function 240 may bedisplayed or otherwise delivered 910, 265 to a recommendationsrecipient. As used herein, a recommendations recipient is an entity whoreceives the adaptive recommendations 250. Thus, the recommendationsrecipient may include the one or more process participants 200 of theadaptive system 100, as indicated by the dotted arrow 910 in FIG. 7, ora non-participant 260 of the associated process (see dotted arrow 265).However, the adaptive recommendations function 240 may also be appliedinternally by the adaptive system 100 to update the structural aspect210 (see dotted arrow 905). In this manner, the usage behavior 270 ofthe one or more process participants 200 may be influenced by the systemstructural alterations that are automatically or semi-automaticallyapplied. Or, the adaptive recommendations function 240 may be used bythe adaptive system 100 to update the content aspect 230 (see dottedarrow 935).

FIG. 10 is a block diagram of the adaptive recommendations function 240used by the adaptive system 100 of FIG. 7. The adaptive recommendationsfunction 240 includes two algorithms, a preference inferencing algorithm242 and a recommendations optimization algorithm 244. These algorithms(which actually may include many more than two algorithms) are used bythe adaptive system 100 to generate adaptive recommendations 250.

Preferably, the adaptive system 100 identifies the preferences of theuser 200 and self-adapts the adaptive system 100 in view of thepreferences. Preferences describe the likes, tastes, partiality, and/orpredilection of the user 200 that may be inferred during access of,interaction with, or while attention is directed to, the objects 212 ofthe adaptive system 100. In general, user preferences exist consciouslyor sub-consciously within the mind of the user. Since the adaptivesystem 100 has no direct access to these preferences, they are generallyinferred by the preference inferencing algorithm 242 of the adaptiverecommendations function 240.

The preference inferencing algorithm 242, infers preferences based, atleast in part, on information that may be obtained as the processparticipant 200 accesses the adaptive system 100. Additional informationmay also be optionally used by the preference inferencing algorithm 242,including meta-information 234 and intrinsic information 232 withinobjects 212, and from information, rules, or algorithms accessed fromother computer-based functions residing within the adaptive system 100,or through access to, or interaction with, other computer-basedfunctions residing outside of the adaptive system 100.

The preference inferencing algorithm and associated output 242 is alsodescribed herein generally as “preference inferencing” or “preferenceinferences” of the adaptive system 100. The preference inferencingalgorithm 242 identifies three types of preferences: explicitpreferences 252, inferred preferences 253, and inferred interests 254.Unless otherwise stated, the use of the term “preferences” herein ismeant to include any or all of the elements 252, 253, and 254 depictedin FIG. 10.

As used herein, explicit preferences 252 describe explicit choices ordesignations made by the user 200 during use of the adaptive system 100.The explicit preferences 252 may be considered to more explicitly revealpreferences than inferences associated with other types of usagebehaviors. A response to a survey is one example where explicitpreferences 252 may be identified by the adaptive system 100.

Inferred preferences 253 describe preferences of the user 200 that arebased on usage behavioral patterns 248. Inferred preferences 253 arederived from signals and cues made by the process participant 200, where“signals” are consciously intended communications by the processparticipant, and “cues” are behaviors that are not intended as explicitcommunications, but nevertheless provide information of a processparticipant with which to infer preferences and interests.

Inferred interests 254 describe interests of the user 200 that are basedon usage behavioral patterns 248. In general, the adaptiverecommendations 250 generated by the adaptive recommendations function240 are derived from the preference inferencing algorithm 242 andcombine inferences from overall user community behaviors andpreferences, inferences from sub-community or expert behaviors andpreferences, and inferences from personal user behaviors andpreferences. As used herein, preferences (whether explicit 252 orinferred 253) are distinguishable from interests (254) in thatpreferences imply a ranking (e.g., object A is better than object B)while interests do not necessarily imply a ranking.

A second algorithm 244, designated recommendations optimization 244,optimizes the adaptive recommendations 250 generated by the adaptiverecommendations function 240 within the adaptive system 100. Theadaptive recommendations 250 may be augmented by automated inferencesand interpretations about the content within individual and sets ofobjects 232 using statistical pattern matching of words, phrases orrepresentations, in written or audio format, or in pictorial format,within the content. Such statistical pattern matching may include, butis not limited to, principle component analysis, semantic networktechniques, Bayesian analytical techniques, neural network-basedtechniques, support vector machine-based techniques, or otherstatistical analytical techniques.

For image-based content, including temporally sequential images as invideo content, convolutional neural networks may be applied in someembodiments to make the inferences or interpretations. The neuralnetworks may be trained by means of a corpus of labeled images and/orsequences of images to identify physical objects, actions, and/orabstract concepts within video-based content. The identification may bedirectly through interpretation of patterns of pixels associated withthe videos and/or through interpretations of audio-based language thatis associated with the images or videos. The labels of identifiedobjects, actions, and/or abstract concepts may be incorporated withinrecommendations 250 that are delivered to users 200.

For text or language-based content (which may originate from audiorecordings), recurrent deep learning-based systems, including longshort-term memory (LSTM) neural networks, and/or associated variationsof LSTM such as Gated Recurrent Units (GRUs), may be applied in someembodiments to make the inferences or interpretations within thetext-based content and/or to make inferences or interpretations aboutother content, such as video, that is associated with the text-basedcontent.

In some embodiments, combinations of deep learning-based neural networksmay be applied to interpret content, such as applying convolutionalneural networks to interpret image-based content, and recurrent neuralnetworks to interpret language-based content.

Adaptive Recommendations

As shown in FIG. 7, the adaptive system 100 generates adaptiverecommendations 250 using the adaptive recommendations function 240. Theadaptive recommendations 250, or suggestions, enable users to moreeffectively use and navigate through the adaptive system 100.

The adaptive recommendations 250 are presented as structural subsets ofthe structural aspect 210. FIG. 11 depicts a hypothetical structuralaspect 210, including a plurality of objects 212 and associatedrelationships 214. The adaptive recommendations function 240 generatesadaptive recommendations 250 based on usage of the structural aspect 210by the one or more process participants 200, possibly in conjunctionwith considerations associated with the structural aspect 210 and thecontent aspect 230.

Three structural subsets 280A, 280B, and 280C (collectively, structuralsubsets 280) are depicted. The structural subset 280A includes threeobjects 212 and two associated relationships, which are reproduced bythe adaptive recommendations function 240 in the same form as in thestructural aspect 210 (objects are speckle shaded). The structuralsubset 280B includes a single object (object is shaded), with noassociated relationships (even though the object originally had arelationship to another object in the structural aspect 210).

The third structural subset 280C includes five objects (stripedshading), but the relationships between objects has been changed fromtheir orientation in the structural aspect 210. In the structural subset280C, a relationship 282 has been eliminated while a new relationship284 has been formed by the adaptive recommendations function 240. Thestructural subsets 280 depicted in FIG. 11 represent but three of amyriad of possible structural subsets that may be derived from theoriginal network of objects by the adaptive recommendations function240.

The illustration in FIG. 11 shows a simplified representation ofstructural subsets 280 being generated from objects 212 andrelationships 214 of the structural aspect 210. Although not shown, thestructural subset 280 may also include corresponding associated subsetsof the usage aspect 220, such as usage behaviors and usage behavioralpatterns. As used herein, references to structural subsets 280 are meantto include the relevant subsets of the usage aspect, or usage subsets,as well.

The adaptive recommendations 250 may be in the context of a currentlyconducted activity or behavior detected by the adaptive system 100, acurrently accessed object 232, or a communication with another processparticipant 200 or non-participant in the process 260. The adaptiverecommendations 250 may also be in the context of a historical path ofexecuted system activities, accessed objects 212, or communicationsduring a specific user session or across user sessions. The adaptiverecommendations 250 may be without context of a current activity,currently accessed object 212, current session path, or historicalsession paths. Adaptive recommendations 250 may also be generated inresponse to direct user requests or queries. Such user requests may bein the context of a current system navigation, access or activity, ormay be outside of any such context.

Adaptive recommendations 250 generated by the adaptive recommendationsfunction 240 may combine inferences from community, sub-community(including expert), and personal behaviors and preferences, as discussedabove, to deliver to the one or more process participants 200, one ormore system structural subsets 280. The process participants 200 mayfind the structural subsets particularly relevant given the currentnavigational context of the user within the system, the physicallocation of the user, and/or a response to an explicit request of thesystem by the one or more users. In other words, the adaptiverecommendation function 240 determines preference “signals” from the“noise” of system usage behaviors.

The sources of user behavioral information, which typically include theobjects 212 referenced by the user 200, may also include the actualinformation 232 contained therein. In generating adaptiverecommendations 250, the adaptive system 100 may thus employ searchalgorithms that use text matching or more general statistical patternmatching to provide inferences on the inferred themes of the information232 embedded in, or referenced by, individual objects 212. Furthermore,the structural aspect 210 may itself inform the specific adaptiverecommendations 250 generated. For example, existing relationshipstructures within the structural aspect 210 at the time of the adaptiverecommendations 250 may be combined with the user preference inferencesbased on usage behaviors, along with any inferences based on the contentaspect 230 (the information 232).

Delivery of Adaptive Recommendations

FIG. 12 is a flow diagram showing how adaptive recommendations 250 aredelivered by the adaptive system 100. Recall from FIG. 7 that adaptiverecommendations 250 may be delivered directly to the one or more users200 (dotted arrow 910), or the adaptive recommendations function 240 maybe applied to automatically or semi-automatically update the structuralaspect 210 (dotted arrow 905) or the content aspect 230 (dotted arrow935), or adaptive recommendations 250 may be delivered directly to thenon-user 260 of the adaptive system 100 (dotted arrow 265).

The adaptive system 100 begins by determining the relevant usagebehavioral patterns 248 (FIG. 9C) to be analyzed (block 283). Theadaptive system 100 thus identifies the relevant communities, affinitygroups, or user segments of the one or more process participants 200.Affinities are then inferred among objects 212, structural subsets 280,and among the identified affinity groups (block 284). This data enablesthe adaptive recommendations function 240 to generate adaptiverecommendations 250 for multiple application purposes. The adaptivesystem 100 next determines whether the adaptive recommendations function240 will generate recommendations 250 to be delivered directly to therecommendations recipients (e.g., 910 to process participants 200 or 265to non-participants 260), or are to be used to update the adaptivesystem 100 (e.g., 905 to the structural aspect 210 or 935 to the contentaspect 230) (block 285). Where the recommendations recipients are todirectly receive the adaptive recommendations (the “no” prong of block285), the adaptive recommendations 250 are generated based on mappingthe context of the current system use (or “simulated” use if the currentcontext is external to the actual use of the system) (block 286) to theusage behavior patterns 248 generated by the preference inferencingalgorithm 242 (block 286).

Adaptive recommendations are then delivered visually and/or in othercommunications forms, such as audio, to the recommendations recipients(block 287). The recommendations recipients may be individual users or agroup of users, or may be non-users 260 of the adaptive system 100. ForInternet-based applications, the adaptive recommendations 250 may bedelivered through a web browser directly, or through RSS/Atom feeds andother similar protocols.

Where, instead, adaptive system 100 itself is to be the “recipient” ofthe adaptive recommendations (the “yes” prong of block 285), theadaptive recommendations function 240 applies the adaptiverecommendations to update the structural aspect 210 (905) or the contentaspect 230 (935). The adaptive recommendations 250 generated by theadaptive recommendations function 240 are determined based, at least inpart, on mapping potential configurations of the structural aspect 210or content aspect 230 to the affinities generated by the usagebehavioral inferences (block 288). The adaptive recommendations 905 or935 are then delivered to enable updating of the structural aspect 210or the content aspect 230 (block 289), respectively.

The adaptive recommendations function 240 may operate completelyautomatically, performing in the background and updating the structuralaspect 210 independent of human intervention. Or, the adaptiverecommendations function 240 may be used by users or experts who rely onthe adaptive recommendations 250 to provide guidance in maintaining thesystem structure as a whole, or maintaining specific structural subsets280 (semi-automatic maintenance of the structural aspect 210).

The navigational context for the recommendation 250 may be at any stageof navigation of the structural aspect 210 (e.g., during the viewing ofa particular object 212) or may be at a time when the recommendationrecipient is not engaged in directly navigating the structural aspect210. In fact, the recommendation recipient need not have explicitly usedthe system associated with the recommendation 250.

Some inferences will be weighted as more important than other inferencesin generating the recommendation 250. These weightings may vary overtime, and across recommendation recipients, whether individualrecipients or sub-community recipients. As an example, thecharacteristics associated with objects 212 which are explicitly storedor tagged by the user 200 in a personal structural aspect 210 wouldtypically be a particularly strong indication of preference as storingor tagging system structural subsets requires explicit action by theuser 200. The recommendations optimization algorithms 244 may thusprioritize this type of information to be more influential in drivingthe adaptive recommendations 250 than, say, general community trafficpatterns within the structural aspect 210.

The recommendations optimization algorithm 244 will particularly try toavoid recommending objects 212 that the process participant or user 200is already familiar with. For example, if the process participant 200has already stored or tagged the object 212 in a personal structuralsubset 280, then the object 212 may be a low ranking candidate forrecommendation to the user, or, if recommended, may be delivered to theuser with a designation acknowledging that the user has already saved ormarked the object for future reference. Likewise, if the user 200 hasrecently already viewed the associated system object (regardless ofwhether it was saved to his personal system), then the object wouldtypically rank low for inclusion in a set of recommended objects.

The preference inferencing algorithm 242 may be tuned by the individualuser. The tuning may occur as adaptive recommendations 250 are providedto the user, by allowing the user to explicitly rate the adaptiverecommendations. The user 200 may also set explicit recommendationtuning controls to adjust the adaptive recommendations to her particularpreferences. For example, the user 200 may guide the adaptiverecommendations function 240 to place more relative weight on inferencesof expert preferences versus inferences of the user's own personalpreferences. This may particularly be the case if the user wasrelatively inexperienced in the corresponding domain of knowledgeassociated with the content aspect 230 of the system, or a structuralsubset 280 of the system. As the user's experience grows, she may adjustthe weighting toward inferences of the user's personal preferencesversus inferences of expert preferences.

Adaptive recommendations, which are structural subsets of the adaptivesystem 100 (see FIG. 11), may be displayed in variety of ways to theuser. The structural subsets 280 may be displayed as a list of objects212 (where the list may be null or a single object). The structuralsubset 280 may be displayed graphically. The graphical display mayprovide enhanced information that may include depicting relationshipsamong objects (as in the “relationship” arrows of FIG. 6).

In addition to the structural subset 280, the recommendation recipientmay be able to access information or logic to assist in gaining anunderstanding about why the particular structural subset was selected asthe recommendation to be presented to the user. The reasoning may befully presented to the recommendation recipient as desired by therecommendation recipient, or it may be presented through a series ofinteractive queries and associated answers, where the recommendationrecipient desires more detail. The reasoning may be presented throughdisplay of the logic of the recommendations optimization algorithm 244.A natural language (e.g., English) interface may be employed to enablethe reasoning displayed to the user to be as explanatory and human-likeas possible.

The personal preference of the user may affect the nature of the displayof the information. For example some users may prefer to see thestructural aspect in a visual, graphic format while other users mayprefer a more interactive question and answer or textual display.

Users of the adaptive system 100, and by extension, process participants200, may be explicitly represented as objects in the structural aspect210 and hence embodied in structural subsets 280. Either embodied asstructural subsets or represented separately from structural subsets280, the adaptive recommendations 250 may include a set of users of theadaptive system 100 that are determined and displayed to recommendationrecipients, providing either implicit or explicit permission is grantedby the set of users to be included in the adaptive recommendations 250.The recommendations optimization algorithm 244 may match the preferencesof other users of the system with the current user. The preferencematching may include applying inferences derived from thecharacteristics of structural subsets stored or tagged by users, theirstructural subset subscriptions and other self-profiling information,and their system usage patterns 248. Information about the recommendedset of users may be displayed. This information may include names, aswell as other relevant information such as affiliated organization andcontact information. The information may also include system usageinformation, such as common system objects subscribed to, etc. As in thecase of structural subset adaptive recommendations, the adaptiverecommendations of other users may be tuned by an individual userthrough interactive feedback with the adaptive system 100.

The adaptive recommendations 250 may be in response to explicit requestsfrom the user. For example, a user may be able to explicitly designateone or more objects 212 or structural subsets 280, and prompt theadaptive system 100 for a recommendation based on the selected objectsor structural subsets. The recommendations optimization algorithm 244may put particular emphasis on the selected objects or structuralsubsets, in addition to applying inferences on preferences from usagebehaviors, as well as optionally, content characteristics.

In some embodiments, the adaptive recommendations function 240 mayaugment the preference inferencing algorithm 242 with considerationsrelated to enhancing the revelation of user preferences, so as to betteroptimize the adaptive recommendations 250 in the future. In other words,where the value of information associated with reducing uncertaintyassociated with user preferences is high, the adaptive recommendationsfunction 240 may choose to recommend objects 212 or other recommendedstructural aspects 210 as an “experiment.” For example, the value ofinformation will typically be highest for relatively new users, or whenthere appears to be a significant change in usage behavioral pattern 248associated with the user 200. The adaptive recommendations function 240may employ design of experiment (DOE) algorithms so as to select thebest possible “experimental” adaptive recommendations, and to optimallysequence such experimental adaptive recommendations, and to adjust suchexperiments as additional usage behaviors 270 are assimilated. In someembodiments, the adaptive recommendations function 240 may apply methodsand systems disclosed in U.S. Provisional Patent Application Ser. No.60/652,578, entitled “Adaptive Decision Process,” filed Feb. 14, 2005,which is incorporated by reference herein, as if set forth in itsentirety.

The preference inferencing 242 and recommendations optimization 244algorithms may also preferentially deliver content that is speciallysponsored; for example, promotional, advertising or publicrelations-related content.

In summary, the adaptive recommendations generated by the adaptiverecommendations function 240 may be delivered 910 to the users 200,delivered 265 to the non-user 260, or delivered 905, 935 back to theadaptive system 100, for updating either the structural aspect 210 (905)or the content aspect 230 (935). The adaptive recommendations 250generated by the adaptive recommendations function 240 will thusinfluence subsequent user interactions and behaviors associated with theadaptive system 100, creating a dynamic feedback loop.

Automatic or Semi-Automatic System Structure Maintenance

The adaptive recommendations function 240, optionally in conjunctionwith system structure maintenance functions that reside within, or areaccessible by, the adaptive computer-based application 925 (not shown),may be used to automatically or semi-automatically update and enhancethe structural aspect 210 of the adaptive system 100. The adaptiverecommendations function 240 may be employed to determine newrelationships 214, or modify existing relationships 214, among objects212 in the adaptive system, within structural subsets 280, or amongstructural subsets associated with a specific sub-community. Theautomatic updating may include potentially assigning a relationshipbetween any two objects to zero (effectively deleting the relationshipbetween the two objects). The modified relationships may represent theworkflow sequencing among objects within the structural aspect 210,where objects represent a process, sub-process or activity.

In either an autonomous mode of operation, or in conjunction with humanexpertise, the adaptive recommendations function 240 may be used tointegrate new objects 212 into the structural aspect 210, or to deleteexisting objects 212 from the structural aspect.

The adaptive recommendations function 240 may also be extended to scanand evaluate structural subsets 280 that have special characteristics.For example, the adaptive recommendations function 240 may suggest thatcertain of the structural subsets that have been evaluated arecandidates for special designation. This may include being a candidatefor becoming a new specially designated sub-system or structural subset.The adaptive recommendations function 240 will present to human users orexperts the structural subset 280 that is suggested to become a newsub-system or structural subset, along with existing sub-system orstructural subsets that are deemed “closest” in relationship to the newsuggested structural subset. A human user or expert may then be invitedto add the object or objects 212, and may manually create relationships214 between the new object and existing objects.

As another alternative, the adaptive recommendations function 240,optionally in conjunction with the system structure maintenancefunctions, may automatically generate the object or objects 212, and mayautomatically generate the relationships 214 between the newly createdobject and other objects 212 in the structural aspect 210.

This capability is extended such that the adaptive recommendationsfunction 240, in conjunction with system structure maintenancefunctions, automatically maintains the structural aspect and identifiedstructural subsets 280. The adaptive recommendations function 240 mayidentify new objects 212, generate associated objects 212, and generateassociated relationships 214 among the new objects 212 and existingobjects 212, but also may identify objects 212 that are candidates fordeletion. The adaptive recommendations function 240 may alsoautomatically delete the object 212 and its associated relationships214.

The adaptive recommendations function 240, in conjunction with systemstructure maintenance functions, may apply “global” considerations andlogic when conducting modifications to the structural aspect 210 toensure effective use and navigation of the structural aspect 210. Forexample, thresholds or limits may guide the absolute number or relativenumber of relationships among objects. Similarly, rules may be appliedto the number of elements in the structural aspect 210 as a whole, orwithin designated subsets of structural aspect 210. Rules related to theduration an object 212 has been incorporated within the structuralaspect 210, or collective quality thresholds for objects 212 may also beapplied. These global rules help ensure that adaptive system 100performs at an optimum possible level of efficiency and effectivenessfor process participants 200 collectively, according to someembodiments.

In this way the adaptive recommendations function 240, optionally inconjunction with a system structure maintenance function, mayautomatically adapt the structural aspect 210 of the adaptive system100, whether on a periodic or continuous basis, so as to optimize theuser experience. In some embodiments, each of the automatic steps listedabove with regard to updating the structural aspect 210 may be employedinteractively by human users and experts as desired.

Hence, the adaptive recommendations function 240, driven in part byusage behaviors, automatically or semi-automatically updates the systemstructural aspect 210 (see dotted arrow 905 in FIG. 7). The feedbackloop is closed as process participant interactions with the adaptivesystem 100 are influenced by the structural aspect 210, providing anadaptive, self-reinforcing feedback loop between the usage aspect 230and the structural aspect 210.

Automatic or Semi-Automatic System Content Maintenance

As shown in FIG. 7, the adaptive recommendations function 240 mayprovide the ability to automatically or semi-automatically update thecontent aspect 230 of the adaptive system 100 (see dotted arrow 935).Examples of on-line content or information 232 within the content aspect230 that may be updated or modified include text, animation, audio,video, tutorials, manuals, executable code, and interactiveapplications. Further, meta-information 234, such as reviews and briefdescriptions of the content may also be updated or modified 935.

The content aspect information items 232 may be directly modified 235 bythe adaptive recommendations function 240. Following are someillustrative examples. For text-based information 232, words or phrasesmay be altered, alternative languages may be applied, and/or theformatting of information 232 may be altered 235. Hyperlinks may beadded or deleted to text-based information 232. For image orgraphical-based information 232, images may be altered 235, orformatting such as color may be adjusted 235. For audio-based orvideo-based information 232, alternative languages may be applied 235and/or alternative sound tracks may be applied 235.

Advertising or promotional elements may be added, deleted, or adjustedwithin information 232.

Customized text or multi-media content suitable for online viewing orprinting may be generated and stored 235 in the content aspect 230. U.S.patent application Ser. No. 10/715,174 entitled “A Method and System forCustomized Print Publication and Management” discloses relevantapproaches for updating the content aspect 230 with adaptive print mediainstances and is incorporated by reference herein, as if set forth inits entirety.

The adaptive recommendations function 240 may operate automatically,performing in the background and updating the content aspect 230independently of human intervention. Or, the adaptive recommendationsfunction 240 may be used by users 200 or special experts who rely on theadaptive recommendations 250 to provide guidance in maintaining thecontent aspect 230.

As in the case of the structural aspect 210, different communities mayalso be used to model the maintenance of the content aspect 230. Thecommunities, affinity groups, and user segments are used to adapt therelevancies and to create, alter or delete relationships 214 between theobjects 212. The adaptive recommendations 250 may present the objects212 to the user 200 in a different combination than initially may havebeen assembled or inputted, and may treat sections of a superordinateobject 212 such as a document, book, manual, video, sound track, orinteractive software as multiple subordinate objects 212 that can berecombined in a pattern that is aligned with community usage, bycreating or altering relationships between sections of the superordinateobject 212.

In addition, as user feedback on system activities and usage behavioralpatterns 248 is accumulated, the adaptive system 100 may suggest areaswhere additional content would be beneficial to users. For example, ifthe object 212 is frequently rated by users 200 as difficult tounderstand, or if only expert users in a community are accessing theobject, the adaptive system 100 may recognize the need for generatingsupplemental content (e.g., in the form of documentation or onlinetutorials or demonstrations), and/or a need to re-structure object 212and/or the associated meta-information 234 or information 232.

The re-structuring 935 of the object 212 may include decomposing theassociated meta-information 234 or information 232 into subordinateobjects 212, and/or meta-information 234 or information 232, andapplying appropriate relationships 214 to these newly created elements.

Hence, as shown in FIG. 7, the adaptive recommendations function 240,driven in part by usage behaviors 270 (see FIG. 9C), automatically orsemi-automatically updates 935 the content aspect 230. The feedback loopis closed as the interactions of the user 200 with the adaptive system100 are influenced by updates to the content aspect 230, providing anadaptive, self-reinforcing feedback loop between the usage aspect 210and the content aspect 230, and, in some embodiments, between the usageaspect 210, the structural aspect 220, and the content aspect 230.

Network-Based Embodiments

The structural aspect 210 of the adaptive system 100 may be based on anetwork structure. The structural aspect 210 thus includes two or moreobjects, along with associated relationships among the objects.Networks, as used herein, are distinguished from other structures, suchas hierarchies, in that networks allow potential relationships betweenany two objects of a collection of objects. In a network, there does notnecessarily exist well-defined parent objects, and associated children,grandchildren, etc., objects, nor a “root” object associated with theentire system, as there would be by definition in a hierarchy. In otherwords, networks may include cyclic relationships that are not permittedin strict hierarchies. As used herein, a hierarchy can be thought of asjust one particular form of a network, with some additional restrictionson relationships among network objects.

The structural aspect 210 of the adaptive system 100 may also have afuzzy network structure. Fuzzy networks are distinguished from othertypes of network structures in that the relationships between objects infuzzy networks may be by degree. In non-fuzzy networks, therelationships between objects are binary. Thus, in non-fuzzy networks,between any two objects relationships either exist or they do not exist.

As used herein, a fuzzy network is defined as a network of informationin which each individual item of information may be related to any otherindividual item of information, and the associated relationship betweenthe two items may be by degree. A fuzzy network can be thought ofabstractly as a manifestation of relationships among fuzzy sets (ratherthan classical sets), hence the designation “fuzzy network.” As usedherein, a non-fuzzy network is a subset of a fuzzy network, in whichrelationships are restricted to binary values (i.e., relationship eitherexists or does not exist.

Generalizing further, both classical networks and fuzzy networks mayhave a-directional (also called non-directed) or directed links betweennodes. Four network topologies are listed in Table 2.

TABLE 2 Network Topologies network type links between nodes link typetype i (classical) binary a-directional type ii (classical) binarydistinctly directional type iii (fuzzy) multi-valued a-directional typeiv (fuzzy) multi-valued distinctly directionalThe first two types (i and ii) are classical networks. Fuzzy networks,as used herein, are networks with topologies iii or iv.

For each of the four network topologies listed in Table 2, anotherpossible variation exists: whether the network allows only a single linkor multiple links between any two nodes, where the multiple links maycorrespond to multiple types of links. For example, the fuzzy networktypes (iii and iv) of Table 2 may permit multiple directionally distinctand multi-valued links between any two nodes in the network. Theadaptive system 100 encompasses any of the network topologies listed inTable 2, including those which allow multiple links and multiple linktypes between nodes.

Mathematically, for a non-fuzzy network, it can be said, without loss ofgenerality, that a relationship translates to either a “0” or a “1”-“0,”for example if there is not a relationship, and “1” if there is arelationship. For fuzzy networks, the relationships between any twonodes, when normalized, may have values along a continuum between 0 and1 inclusive, where 0 implies no relationship between the nodes, and 1implies the maximum possible relationship between the nodes.

The structural aspect 210 of the adaptive system 100 of FIG. 7 maysupport any of the network topologies described above. A-directionalrelationships between nodes (no arrows), directed relationships betweennodes (whether single- or double-arrow), and multiple types ofrelationships between nodes, are supported by the adaptive system 100.Further, relationship indicators which are binary (e.g., 0 or 1) ormulti-valued (e.g., range between 0 and 1) are supported by the adaptivesystem.

It can readily be seen that a hierarchy may be described as a directedfuzzy network with the additional restrictions that the relationshipvalues and indicators associated with each relationship must be either“1” or “0” (or the symbolic equivalent). Further, hierarchies do notsupport cyclic or closed relationship paths.

FIG. 13 illustrates a fuzzy network 500, including a subset 502 of fuzzynetwork 500. The subset 502 includes three objects 504, 506, and 508,designated as shaded for ease of identification. The subset 502 alsoincludes associated relationships (arrows) and relationship indicatorsor weightings (values) among the three objects. The separated subset ofthe network 502 yields a fuzzy network (subset) 500 s.

A particular implementation of a fuzzy network structure, a fuzzycontent network, which may advantageously constitute the fuzzy network500, is disclosed in U.S. Pat. No. 6,795,826, entitled “Fuzzy ContentNetwork Management and Access,” and is incorporated by reference herein,as if set forth in its entirety.

The adaptive system 100 of FIG. 7 may utilize fuzzy network structures,such as the fuzzy network 500 of FIG. 13. In FIG. 14, an adaptive system100C includes a structural aspect 210C that is a fuzzy network 500.Thus, adaptive recommendations 250 generated by the adaptive system 100Care also structural subsets that are themselves fuzzy networks. Further,although not explicitly shown in FIG. 14, the usage aspect 220 may alsobe entirely, or in part, represented by a fuzzy network.

The structural aspect 210 of the adaptive system 100 may includemultiple types of structures, comprising network-based structures,non-network-based structures, or combinations of network-basedstructures and non-network-based structures. In FIG. 15, the adaptivesystem 100D includes a structural aspect 210D, which includes multiplenetwork-based structures and non-network-based structures. The multiplestructures of 210D may reside on the same computer system, or thestructures may reside on separate computer systems.

Adaptive Recombinant Systems

In FIG. 16, according to some embodiments, a particular configuration ofthe adaptive recombinant computer-based application 925R (FIG. 4C) isdepicted, in which the adaptive recombinant computer-based application925R includes an adaptive recombinant system 800. The adaptiverecombinant system 800 includes the adaptive system 100 of FIG. 7, aswell as the adaptive recombinant function 850. The adaptive recombinantfunction 850 includes a syndication function 810, a fuzzy networkoperators function 820, and an object evaluation function 830. Just asthe adaptive system 100 may be part of the adaptive process 900, theadaptive recombinant system 800 may be part of the adaptive recombinantprocess 901. The adaptive recombinant function 850, including thesyndication function 810, the fuzzy network operators function 820, andthe object evaluation function 830 functions may all reside within theadaptive recombinant computer-based application 925R, as shown in FIG.16, or one or all of the functions may be external to the computer-basedapplication 925R.

The adaptive recombinant system 800 is capable of syndicating andrecombining structural subsets 280. The structural subsets 280 may bederived through either direct access of the structural aspect 210 by thefuzzy network operators function 820, or the structural subsets 280 maybe generated by the adaptive recommendations function 240. The adaptiverecombinant system 800 of FIG. 16 is capable of syndicating (sharing)and recombining the structural subsets, whether for display to the user200 or non-user 260, or to update the structural aspect 210 and/or thecontent aspect 230 of the adaptive system 100. In addition, thesefunctions are capable of accessing and updating multiple adaptivesystems 100, or aiding in the generation of a new adaptive system 100.

The syndication function 810 may syndicate elements of the usage aspect220 associated with syndicated structural subsets 280, thus enablingelements of the usage clusters and patterns, along with thecorresponding structural subsets, to be combined with other structuralsubsets and associated usage clusters and patterns.

As explained above, the structural aspect 210 of the adaptive system 100may employ a network structure, and is not restricted to a particulartype of network. In some embodiments, the adaptive recombinant system800 operates in conjunction with an adaptive system in which thestructural aspect 210 is a fuzzy network. The structural subsets 280generated by the adaptive recombinant system 800 during syndication orrecombination are likewise fuzzy networks in these embodiments, and arealso called adaptive recombinant fuzzy networks. Recall that astructural subset is a portion or subset of the structural aspect 210 ofthe adaptive system 100. The structural subset 280 may include a singleobject, or multiple objects, and, optionally, their associatedrelationships.

The adaptive recombinant system 800 of FIG. 16 is able to syndicate andcombine structural subsets 280 of the structural aspect 210 (where astructural subset 280 may contain the entire structural aspect 210). Thestructural subsets 280, which are fuzzy networks, in some embodiments,may be syndicated in whole or in part to other computer networks,physical computing devices, or in a virtual manner on the same computingplatform or computing network. Although the adaptive recombinant system800 is not limited to generating structural subsets which are fuzzynetworks, some of the following figures and descriptions, used toillustrate the concepts of syndication and recombination, feature fuzzynetworks. Designers of ordinary skill in the art will recognize that theconcepts of syndication and recombination may be generalized to othertypes of networks.

Thus, the adaptive recombinant system 800 of FIG. 16 may utilize fuzzynetwork structures. In FIG. 17, an adaptive recombinant system 800Cincludes the adaptive system 100C of FIG. 14, in which the structuralaspect 210C is a fuzzy network. Thus, the adaptive recombinant system800C may perform syndication and recombination operations, as describedabove, to generate structural subsets that are fuzzy networks.

Fuzzy Network Subsets and Adaptive Operators

The adaptive recombinant system 800 of FIG. 16 includes fuzzy networkoperators 820. The fuzzy network operators 820 may manipulate one ormore fuzzy or non-fuzzy networks. Some of the operators 820 mayincorporate usage behavioral inferences associated with the fuzzynetworks that the operators act on, and therefore these operators may betermed “adaptive fuzzy network operators.” The fuzzy network operators820 may apply to any fuzzy network-based system structure, includingfuzzy content network system structures, described further below.

FIG. 18 is a block diagram depicting some fuzzy network operators 820,also called functions or algorithms, used by the adaptive recombinantsystem 800. A selection operator 822, a union operator 824, anintersection operator 826, a difference operator 828, and a complementoperator 832 are included, although additional logical operations may beused by the adaptive recombinant system 800. Additionally, the fuzzynetwork operators 820 include a resolution function 834, which is usedin conjunction with one or more of the operators in the fuzzy networkoperators 820.

A selection operator 822, which selects subsets of networks, maydesignate the selected network subsets based on degrees of separation.For example, subsets of a fuzzy network may be selected from theneighborhood, around a given node, say Node X. The selection may takethe form of selecting all nodes within the designated networkneighborhood, or all the nodes and all the associated links as wellwithin the designated network neighborhood, where the networkneighborhood is defined as being within a certain degree of separationfrom Node X. A non-null fuzzy network subset will therefore contain atleast one node, and possibly multiple nodes and relationships.

Two or more fuzzy network subsets may then be operated on by networkoperations such as union, intersection, difference, and complement, aswell as any other network operators that are analogous to Boolean setoperators. An example is an operation that outputs the intersection(intersection operator 826) of the network subset defined by the firstdegree or less of separation from Node X and the network subset definedby the second or less degree of separation from Node Y. The operationwould result in the set of nodes and relationships common to these twonetwork subsets, with special auxiliary rules optionally applied toresolve duplicative relationships as explained below.

The fuzzy network operators 820 may have special capabilities to resolvethe situation in which union 824 and intersection 826 operators definecommon nodes, but with differing relationships or values of therelationships among the common nodes. The fuzzy network intersectionoperator 826, Fuzzy_Network_Intersection, may be defined as follows:

Z=Fuzzy_Network_Intersection(X,Y,W)

where X, Y, and Z are network subsets and W is the resolution function834. The resolution function 834 designates how duplicativerelationships among nodes common to fuzzy network subsets X and Y areresolved.

Specifically, the fuzzy network intersection operator 826 firstdetermines the common nodes of network subsets X and Y, applying theobject evaluation function 830 to determine the degree to which nodesare identical, to form a set of nodes, network subset Z. The fuzzynetwork intersection operator 826 then determines the relationships andassociated relationship value and indicators uniquely deriving from Xamong the nodes in Z (that is, relationships that do not also exist inY), and adds them into Z (attaching them to the associated nodes in Z).The operator then determines the relationships and relationshipindicators and associated values uniquely deriving from Y (that is,relationships that do not also exist in X) and applies them to Z(attaching them to the associated nodes in Z).

For relationships that are common to X and Y, the resolution function834 is applied. The resolution function 834 may be any mathematicalfunction or algorithm that takes the relationship values of X and Y asarguments, and determines a new relationship value and associatedrelationship indicator.

The resolution function 834, Resolution_Function, may be a linearcombination of the corresponding relationship value of X and thecorresponding relationship value of Y, scaled accordingly. For example:

Resolution_Function(X _(RV) ,Y _(RV))=(c ₁ *X _(RV) +c ₂ *Y _(RV))/(c ₁+c ₂)

where X_(RV) and Y_(RV) are relationship values of X and Y,respectively, and c₁ and c₂ are coefficients. If c₁=1, and c₂=0, thenX_(RV) completely overrides Y_(RV). If c₁=0 and c₂=1, then Y_(RV)completely overrides X_(RV). If c₁=1 and c₂=1, then the derivedrelationship is a simple average of X_(RV) and Y_(RV). Other values ofc₁ and c₂ may be selected to create weighted averages of X_(RV) andY_(RV). Nonlinear combinations of the associated relationships values,scaled appropriately, may also be employed.

The Fuzzy_Network_Union operator 824 may be derived from theFuzzy_Network_Intersection operator 826, as follows:

Z=Fuzzy_Network_Union(X,Y,W)

where X, Y, and Z are network subsets and W is the resolution function834. Accordingly,

Z=Fuzzy_Network_Intersection(X,Y,W)+(X−Y)+(Y−X)

That is, fuzzy network unions of two network subsets may be defined asthe sum of the differences of the two network subsets (the nodes andrelationships that are uniquely in X and Y, respectively) and the fuzzynetwork intersection of the two network subsets. The resulting networksubset of the difference operator contains any unique relationshipsbetween nodes uniquely in an originating network subset and the fuzzynetwork intersection of the two subsets. These relationships are thenadded to the fuzzy network intersection along with all the unique nodesof each originating network subset, and all the relationships among theunique nodes, to complete the resulting fuzzy network subset.

For the adaptive recombinant system 800, the resolution function 834that applies to operations that combine multiple networks mayincorporate usage behavioral inferences related to one or all of thenetworks. The resolution function 834 may be instantiated directly bythe adaptive recommendations function 240 (FIG. 16), or the resolutionfunction 834 may be a separate function that invokes the adaptiverecommendations function. The resulting relationships in the combinednetwork will therefore be those that are inferred by the system toreflect the collective usage histories and preference inferences of thepredecessor networks.

For example, where one of the predecessor networks was used by largernumbers of individuals, or by individuals that members of communities oraffinity groups that are inferred to be best informed on the subject ofthe associated content, then the resolution function 834 may choose topreferentially weight the relationships of that predecessor networkhigher versus the other predecessor networks. The resolution function834 may use any or all of the usage behaviors 270, along with associateduser segmentations and affinities obtained during usage behaviorpre-processing 204 (see FIG. 9C), as illustrated in FIG. 6 and Table 1,and combinations thereof, to determine the appropriate resolution ofcommon relationships and relationship values among two or more networksthat are combined into a new network.

The object evaluation function 830 may applied when the adaptiverecombinant system 800 of FIG. 16 is used to combine networks. Combiningnetworks requires a determination of which objects 212 in two or morenetworks are identical, or near enough to being identical to beconsidered identical, for the purposes of combining the networks. Insome embodiments, the object evaluation function 830 may enable a globalidentification management process in which each object 212 has a uniquesystem designator, which enables direct determination of identity of theobjects. This approach may be augmented by the tracking versions orgenerations of objects 212, such that the adaptive recombinant system800 has options for using more recent versions of an object 212 whennetworks are combined. In other embodiments, the object evaluationfunction 830 may compare the intrinsic information associated with twoobjects 212 to determine whether they are identical or nearly identicalenough to be considered identical for the purposes of combining thenetworks. For example, for text-based objects 212, associatedmeta-information 234 or information 232 may be compared between twoobjects using text-based pattern matching or statistical algorithms. Foraudio or video-based objects 212, other appropriate pattern matchingalgorithms may be applied by the object evaluation function 830 to theassociated meta-information 234 or information 232

Fuzzy Process Networks

In some embodiments, implementation of a fuzzy network-based process maybe through connecting an existing or new process with a fuzzy network500A, as is shown in FIG. 19A. For example, an activity 45 within aprocess or sub-process 136 may precede another activity 50 in thesub-process, with an explicit workflow 55 between the activities. Itshould be understood that there may be a greater number of activities inthe process or sub-process 136 than the minimal number illustrated inFIG. 19A. The fuzzy content network 500A, managed by the adaptivecomputer-based application 925, which is “external” to the activities45, 50 in the sub-process 136, may be accessible 56, 57 by one or moreof the activities 45, 50.

In other embodiments, implementation of a fuzzy network-based processmay be through including an existing or new process within a fuzzynetwork 500B managed by the adaptive computer-based application 925, asis shown in FIG. 19B. For example, an activity 65 within a process orsub-process 137 may precede another activity 70 in the sub-process, withan explicit workflow 75 between the activities 75. These activities andtheir relationships are represented directly within the fuzzy network500B in this case. It should be understood that there may be a greaternumber of activities in the process/sub-process 137 than the minimalnumber illustrated in FIG. 19B.

In some embodiments, adaptive recombinant processes may employstructures based on fuzzy content networks, as defined in U.S. Pat. No.6,795,826, entitled “Fuzzy Content Network Management and Access.” Thesestructures may include the use or adaptation of fuzzy content networksand associated topic objects and content objects, as defined therein.

For “inclusive” fuzzy network embodiments, such as the fuzzy contentnetwork 500B of FIG. 19B, according to some embodiments, FIG. 20Adepicts the structure of a process topic object 445 t, which consists ofmeta-information 450 t only, and is analogous to a fuzzy content networktopic object. Likewise, FIG. 20B depicts a process content object 445 c,which consist of embedded information, or references (for example,pointers or URLs) to information 455 c, and the associatedmeta-information 450 c. Fuzzy process content objects 455 c areanalogous to fuzzy content network content objects. According to someembodiments, process activities may be included within the fuzzy contentnetwork, and as shown in FIG. 21A, and a process activity object 445 acontains meta-information 450 a, analogous to the process topic object455 t of FIG. 20A. In other embodiments, as shown in FIG. 21B, processactivities may be included within the fuzzy content network, and aprocess activity object 446 a will contain meta-information 451 a, aswell as information or a pointer to information 456 a, analogous to theprocess content object 445 c of FIG. 20B. For all of these fuzzy networkobject structures, relationships and associated relationship indicatorsmay be established between any two process objects in the processnetwork, and there may be plurality of types of relationships andassociated relationship indicators between any two process objects. Insome embodiments, at least one relationship type denotes processsequence or workflow, and is typically applied among process activityobjects, but may apply among other process objects as well.

As reviewed previously, FIGS. 20A, 20B, 21A and 21B depict in someembodiments how fuzzy network objects may be converted to processnetwork objects, and how special process objects, process activityobjects 445 a and 446 a may be defined.

FIG. 22A illustrates a process activity “network A” 460, including fouractivities (465 a, 465 b, 465 c, and 465 d) and work flow relationshipsamong the activities (470 a, 470 b, 470 c, and 470 d), as well asrelationships to activities external to process activity “network A” 470e. Each relationship has an associated relationship indicator 471. Insome embodiments, the relationship indicator is represented in the form:

Sequence(Relationship type,First Activity,Second Activity)

The relationship indicator “S(1,1,2)” 470 of relationship 470 a thusimplies a relationship of type 1 between activity 1 and activity 2, inthat sequence.

FIG. 22B illustrates a process activity network 475, which may havemultiple relationship types 476 a and 476 b outbound from an activity(activity 1 474 a), and may also have multiple relationship typesinbound 476 b and 476 c to an activity (activity 4 474 b). Furthermore,multiple relations of different relationship types may be outbound fromone or more activities in the process activity network to destinationsoutside the process activity network. For example, in FIG. 22B,relationship 476 d of relationship type 2 (S(2,4,M)) is outbound fromactivity 4 474 b; likewise, relationship 476 e having relationship type1 (S(1,4,N)) is also outbound from activity 4 474 b.

According to some embodiments, FIGS. 23A and 23B depict process networks480A and 480B (collectively, process network 480). The process networks480A and 480B are depicted for a particular relationship and associatedrelationship indicators, at particular times (t₀ and t₂), in someembodiments. The process networks 480A and 480B are process activitynetworks (see FIGS. 22A and 22B). The process networks 480A and 480B areintegrated with process content objects, for example, “content object 1”485 a and process topic objects, for example, “topic object 1” 485 b.Relationships and associated relationship indicators may exist betweenprocess activity objects and process content or topic objects, forexample, 490.

FIG. 24 is a flow diagram illustrating how process usage informationassociated with the process networks 480A and 480B are processed,according to some embodiments, over a period of time. During time t₁,usage behavior information 920 is tracked and processed (block 4495).The adaptive recommendations function 240 of the adaptive system 100 isinvoked (block 4500), and the process structure of the process network480A is automatically or semi-automatically updated (block 4505),resulting in process network 480B at time t₂. Thus, process network 480Aat time t₀ (FIG. 23A) automatically or semi-automatically becomesprocess network 480B at time t₂ (FIG. 23B), using the procedure in FIG.24. Structures that may be updated within the process network 480include relationship indicators; for example, relationship indicators515 between content object 1 485 a and activity 1 520 had values of 0.4and 0.6 at time to (FIG. 23A); at time t₂, the relationship indicators515 have values of 0.8 and 0.6 (FIG. 23B). Relationships may be deleted,as for example between process activity 1 520, and process activity 4525 (formerly S(2,1,2) in FIG. 23A). Relationships and associatedrelationship indicators may be added, as for example 530 betweenactivity 4 525 and content object 4 540. And process objects, andassociated relationships may be deleted. For example the former contentobject 5 of FIG. 23A and its associated relationships and relationshipindicators, is not part of process network 480B.

FIG. 25 depicts process network 480B (FIG. 24B) at time t₂. Processactivity objects (shaded) are selected, along with the associatedrelationships between these process activity objects, as well as otherselected process objects that have a relationship to the selectedprocess activity objects, and the associated relationships. In someembodiments, the selection of the process network subset may be throughapplication of network neighborhood metrics, such as degrees ofseparation metrics, or fuzzy degrees of separation network neighborhoodmetrics. In other embodiments, other selection methods may be used,including individually specifying process objects and associatedrelationships. In this example, the result of the selection/sub-setting555 of process network 480B is process network 560.

Adaptive Recombinant Processes

FIGS. 26 and 27 illustrate the syndication and combination of processnetworks by the adaptive recombinant system 800C. (The process networkactivity objects are shaded, to distinguish from the content and topicobjects.) In FIG. 26, process network subset B 560 (FIG. 25) issyndicated to an existing process network C 580 that may exist on thesame computer system or a different computer system. It should be notedthat a process network need not represent a “complete” or “functional”process. For example, process network C 580 contains two processactivity objects 581, 582 that do not have a direct relationship to oneanother. In addition, associated relationships 581 r and 582 r have nocorresponding forward sequence process activity object within theprocess network 580. In general, a process network may be fragmentary,without completeness of process objects and relationships.

FIG. 27 illustrates the results of the combination of process network B560 and process network C 580 by the adaptive recombinant system 800C,and the application of the fuzzy network operators function 820, theadaptive recommendations function 240 and the object evaluation function830 (FIG. 17). The result is process network D 590. Note that alldistinct process activity objects from 560 and 580 reside in 590, andthe associated relationships among the process activity objects areresolved and established. Note also that these relationships may bereflexive, as in the case of 591 and 592. In the process network subsetC 580 (FIG. 26), a relationship indicator “S(2,M,4)” is indicated,although no “activity 4” is present in the sub-network 580. Oncesyndication with process network subset B 560, which includes “Activity4,” occurs, the adaptive recombinant system 800C automatically relatesthe two activities 4 and M, as shown in FIG. 27. Other process objectsand corresponding relationships may be resolved as previously described.

FIG. 28 illustrates that the process network 560 may be encompassed bythe structural aspect 210C of adaptive system 100C (FIG. 7). The processnetwork 560 may be the sole content network within structural aspect210C, or may be one of multiple network or non-network structures within210C, as is more generally depicted in FIG. 15, above.

Likewise, FIG. 29 illustrates that the process network 560 may beencompassed by the structural aspect 210C of the adaptive system 100C,which may form part of the adaptive recombinant system 800C. Again, theprocess network 560 may be the sole content network within structuralaspect 210C, or may be one of multiple networks within 210C, and may besyndicated, modified, and combined with other content or processnetworks, as is more generally depicted in FIGS. 47 and 48, below. Theprocess network 560 or another process network structure within thestructural aspect 210C may correspond to the adaptive process instance930 of FIGS. 4A and 4B, and hence FIGS. 15, 29, 47 and 48 illustrate theability to syndicate and combine representations of adaptive processinstances 930, thereby enabling the adaptive recombinant process 901.

FIGS. 30A, 30B, 31A, and 31B illustrate the general approachesassociated with process network syndication and combinations, as managedby the adaptive recombinant system 800C, and applied as part of aparticular type of application of the adaptive recombinant process 901,designated in FIGS. 30A, 30B, 31A and 31B as process application type901A. FIG. 30A illustrates a hypothetical starting condition, anddepicts three organizations, 650, 655, 660. These may be organizations(which may be individuals) within the same business or institution, orone or more may be in businesses or institutions external to the others.A first process network, “process network 1” 665, is used solely by, orresides within, “organization 1” 650. A second process network, “processnetwork 2” 670, is used solely by, or resides within, “organization 2”655. “Organization 3” 660 does not have a process network initially, inthis example.

FIG. 30B illustrates that a subset of “process network 1” 665 isselected to form “process network 1A” 680. “Process network 1A” 680 isthen syndicated as “process network 1A” 685 to “organization 2.”“Organization 2” 655 then syndicates “process network 1A” 685 to“organization 3” 660 as “process network 1A” 690. Thus, FIG. 30Billustrates how process networks, or subsets of process networks, can besyndicated among organizations without limit. FIG. 31A depicts a subsetof “process network 1” 665 and “process network 1A” 695 residing in“organization 1,” in which “process network 1a” 695 is syndicated to“organization 2” 655 as “process network 1A” 700. “Process network 1A”700 and the existing “process network 2” 670 in “organization 2” arecombined 710 to form “process network 2a” 715 in organization 2 655.“Process network 2a” 715 is then syndicated to “organization 3” 660 asprocess network 2A 720.

FIG. 31B represents a continuation of FIG. 31A, in which additionalcombination and syndication takes place. “Process network 2a” 720 in“organization 3” 660 is syndicated to “organization 1” 650 as processnetwork 2A 730. Process network 2A 730 is then combined with theoriginal “process network 1” 665 in “organization 1” 650 to generate“process network 3” 740 in “organization 1” 650.

FIGS. 30A, 30B, 31A, and 31B demonstrate that, in some embodiments,adaptive recombinant processes may indefinitely enable sub-setting ofprocess networks, syndicating the subsets to one or more destinations,and enabling the syndicated process networks to be combined with one ormore process networks at the destinations. At each combination step, therelationship resolution function 834 (of the fuzzy network operators820—see FIG. 18) and the adaptive recommendations function 240 may beinvoked to create and update process structure (and content) asappropriate.

According to some embodiments, FIG. 32 depicts possible deployments ofprocess networks within and across organizations or businessenterprises. In FIG. 32, two enterprises 1810, 1815 are depicted, but itshould be understood the following described process and process networktopologies can apply to any plurality of organizations, individuals, orbusiness enterprises. One topology is represented by “Process 1” 1811containing one process network, 1812, within one enterprise, 1810. Inanother topology, a process 1816 contains a plurality of processnetworks 1817, 1818 within one business enterprise, 1815. In anothertopology, a process 1820 may extend across more than one enterprise 1810and 1815, and may contain a plurality of process networks 1821, 1822,and 1823. A process network 1823 may extend across business enterprises1810 and 1815. Process networks may have common subsets, as exemplifiedby 1822 and 1823. Processes and process networks may extend across anunlimited number of organizations or business enterprises as depicted byprocess 1830 and process network 1832.

According to some embodiments, FIG. 33 depicts a process networktopology in which a process network 1840 includes multiple processes,each process contained partially or as a whole within the processnetwork 1840, and include a multiplicity of other process networks, eachprocess contained partially or as a whole, where each contained processor process network may span a plurality of organizations or businessenterprises.

Process Lifecycle Framework

In some embodiments, as shown in FIG. 34, a process lifecycle framework3000 may be used as an implementation framework for migrating toadaptive processes, based on the implementation of adaptive recombinantprocesses, or other methods and technologies.

The process lifecycle framework 3000 has two primary dimensions. Thehorizontal dimension denotes how the organizing topology 3010 of aprocess is managed—either in a centralized 3011 or decentralized 3012manner. The vertical dimension relates to the local differentiation 3020of a process—how differentiated 3021 or customized 3022 the process isfor local applications or implementations. The process may bestandardized across all local applications 3021, or customizable tolocal applications 3022. The intersections of these dimensions denotefundamental process lifecycle positions. For example, a centralizedorganizing topology, coupled with standardization of processes acrosslocal applications, may be called a “cost and control” quadrant 3030.The focus in this quadrant is typically to ensure low cost processesthat enforce broad standards across organization and application areas.This is the typical architecture of prior art processes supported byEnterprise Resource Planning (ERP) software that are implemented on atruly enterprise basis.

A decentralized organizing topology, coupled with standardization ofprocesses across local applications, may be called the “ad hoc” quadrant3040. The focus in this quadrant is to enforce broad standards acrossorganization and application areas, but through a decentralized processmanagement and infrastructure approach. This quadrant often representsan inconsistency of objectives, and may be the result of organizationalcombinations, such as through a merger or acquisition. It is oftendesirable to not remain in this quadrant in the long-term, as ad hocimplementation typically generates more costs to deliver the sameresults as the “cost and control” quadrant 3030.

A decentralized organizing topology, coupled with customization ofprocesses across local applications, may be called the “NicheAdvantages” quadrant 3050. The emphasis of this quadrant is to maximizethe value of the process in specific application areas through adecentralized process management and infrastructure approach thatenables maximum flexibility and tailoring to local needs. This quadrantrepresents a potentially high value, but also high cost approach. It isoften consistent with the development of new processes that providecompetitive advantages, where the generation of value from the processesoverrides inefficiencies stemming from decentralized process managementand heterogeneous enabling infrastructure. Over time, however, ascompetitive advantages potentially dissipate, the cost penaltyassociated with this quadrant may be too high compared to the derivedbenefits.

A centralized organizing topology, coupled with customization ofprocesses across local applications, may be called the “AdaptiveProcesses” quadrant 3060. The emphasis of this quadrant is to maximizethe value of the process in specific application areas, but through anefficient, centralized process management and infrastructure approachthat enables maximum flexibility and tailoring to local needs. Thisquadrant represents a potentially high value and low cost approach, andprovides advantages versus the other three quadrants. An adaptiveprocess approach has been very difficult to achieve with prior artprocess and supporting process infrastructure and systems. The adaptiveprocesses quadrant 3060 is the quadrant, in particular, that adaptiverecombinant processes advantageously addresses.

According to some embodiments, FIG. 35 is a framework 3100 thatdescribes how processes typically include multiple functionality layers3110. For example, these layers may comprise information technologylayers, with the highest level corresponding to process work flow andbusiness logic, and lower layers corresponding to more generalizedinformation technology, such as content management, database managementsystems, and communications networks.

In a process implementation, then, different layers may have differentprocess lifecycle quadrants. For example, the top-most layer may be aniche advantage quadrant 3120, the directly supporting layer may be anadaptive processes quadrant 3130, and the directly supporting layer ofthat layer may be a cost and control quadrant 3140. In general, it isgood practice that the lower process layers should be at least asstandardized as the layers above.

According to some embodiments, FIG. 36 represents a process lifecyclemanagement framework 3200 that may be advantageously used by businessesand institutions to ensure the highest possible value from theirprocesses over time. The framework 3200 may be understood to representone specific process lifecycle functionality layer.

Business innovations 3210 may be the source of processes (or processfunctionality layers) in the Niche Advantages quadrant. Businesscombinations 3230 may be the source of processes in the Ad HocImplementation quadrants. It is usually advantageous to migrate from theAd Hoc Implementation quadrant to the Cost and Control quadrant throughmore effective leverage of scale 3240. It may be advantageous to migratefrom the Niche Advantages quadrant to the Adaptive Processes quadrantthrough leverage of mass customization techniques 3220. It may also beadvantageous to migrate from the Cost and Control quadrant to theAdaptive Processes quadrant through leverage of mass customizationtechniques 3250. Alternatively, it may also be advantageous toexternalize the process 3260 from the Cost and Control quadrant, whereexternal sources can provide process advantages, typically eitherthrough cost effectiveness, or through more effective customization oradaptation to local applications and the same cost.

Adaptive Process Application Areas

Recall from FIGS. 3, 4A, 4B, and 4C that adaptive recombinant processesmay be applied to improve the functionality of any process 168 byintegrating adaptive recommendations functions into the process 168 andapplying the adaptive recommendations to facilitate the more effectiveuse of the process instance 930. The application of the adaptiverecommendations may be through delivery of adaptive recommendations 910to process participants 200 or by applying the adaptive recommendationsto modify the structure 905 and/or content 935 of computer-basedapplications 175 supporting the process, or both.

Adaptive Asset Management

According to some embodiments, the adaptive process 900 may be used toestablish online asset management systems and processes. An on-lineasset is defined as any item of software or content, or any tangible orintangible asset that the software or on-line content represents. Inother words, the asset to be managed may also be derivative from therepresentations of the software or content of adaptive process 900.

Recall from FIGS. 4A and 4B that the adaptive computer-based application925 may integrate with existing and/or new online computer applications175 to enable capture and analysis of usage behavior information 920.This information may then be used to determine the value of the onlinecomputer and software assets. This determination of value of onlineassets can then be applied beneficially to facilitate asset managementprocesses associated with the on-line assets, optionally includingapplying a function to automatically or semi-automatically modify theone or more computer applications 175 in alignment with the inferredvalue of the online assets of computer applications 175 to processparticipants 200.

FIG. 37 depicts an adaptive process 900A, including an adaptive assetmanagement system 1500. The asset management system 1500 includes theadaptive computer-based application 925 and an asset management function1510. Although in FIG. 37, the asset management function 1510 is shownto be external to the adaptive computer-based application 925, it shouldbe understood that the asset management function 1510 may be configuredto be internal to the adaptive computer-based application 925. Further,although not shown in FIG. 37, the adaptive computer-based application925 may contain the adaptive system 100.

The asset management function 1510 receives information 1520 associatedwith data regarding the usage behaviors 920 of process participants 200,or inferences of the preferences and interests of online assetsassociated with the process participant usage behaviors 920. The assetmanagement function 1510 uses the information 1520 to derive the valueof online assets. The derived value may be of different magnitudes fordifferent individuals or communities of process participants 200. Theasset valuation information determined by the asset management function1510 may be applied to decide near-term or long-term online assetchanges and directions. For example, a high-value on-line asset might bemade more prominently available for process participants 200, while lessvaluable assets might be made less prominent, or eliminated from thecontent and computer applications 175. New development projects todeliver on-line assets that are expected to be of high value based onthe valuations of the asset management function 1510 may be conducted.Further, in addition to on-line assets, features associated with theassets may be evaluated by the asset management function 1510, andappropriate asset modifications or development projects initiated. Forsome modifications, the asset management function 1510 may be used tosupport making the appropriate changes.

The asset management function 1510 may automatically orsemi-automatically modify 1505 the adaptive computer-based application925. For alternative embodiments in which the asset management function1510 is internal to the adaptive computer-based application 925, theadaptive self-modification operation 1505 is analogous to the structuralmodifications 905 of the adaptive system 100, the adaptive recombinantsystem 800, and the generalized adaptive computer-based application 925,described above. Likewise, the asset management function 1510 mayautomatically or semi-automatically modify 1515 content within adaptivecomputer-based application 925. For embodiments in which the assetmanagement function 1510 is internal to the adaptive computer-basedapplication 925, the adaptive self-modification of content 1515 isanalogous to the content-based modifications 935, 905 of theaforementioned systems 100, 800, 925 (represented in parentheses).Further, other computer applications and content 175 may beautomatically or semi-automatically modified 1525 by the assetmanagement function 1510 in accordance with valuations derived by assetmanagement function 1510. In such cases, even if direct usage behavioralinformation 920 are not available for non-adaptive computer application181 and content 180, the asset management function 1510 may makeinferences based on analogy from interactions of the processparticipants 200 with the adaptive computer-based application 925 togenerate appropriate valuations.

Note that adaptive recommendations 910 delivered to process participants200 is not an essential feature for enabling process application 900A.

Adaptive Real-Time Learning

The adaptive process 900 may be used to establish an adaptive processenvironment 930 (FIGS. 4A and 4B) to promote enhanced learning byprocess participants 200, including real-time learning, for existing ornew processes through the implementation of adaptive recommendations 910that are delivered directly to the process participant or user 200, orindirectly through adaptive modification of the process networkstructure 905 or content 935. In some embodiments, the resultingenvironment may be metaphorically termed an adaptive online “cockpit” ofprocess knowledge and activities that effectively “surrounds” theprocess user. This approach facilitates the real-time learning ofprocess participants 200, rather than relying solely or primarily onclassroom or other episodic forms of education or training.

FIG. 38 illustrates an adaptive process 900B, or adaptive real-timelearning process, including an exemplary process participant interface1600 associated with a computing device 964 that is interacted with byprocess participants 200. It should be understood that although FIG. 38illustrates a visual, display-oriented process participant interface,the interface could be audio-based, tactile or kinesthetically-based, orthe interface could be comprised of combinations of visual, audio, orkinesthetic elements. The process participant interface 1600 of theadaptive process 900B may include one or more instances of displayedadaptive recommendations 910 associated with the adaptive computer-basedapplication 925, in which the adaptive recommendations 910 are formattedfor viewing in a specified manner. In FIG. 38, a first formattedinstance 1610 and a second formatted instance 1620 of adaptiverecommendations 910 are shown. The process participant interface 1600may contain other information 915 derived from the adaptivecomputer-based application 925, formatted as appropriate for display. Aformatted instance 1630 of information 915 from the adaptivecomputer-based application 925 is shown. A formatted instance 1630 maycontain one or more instances of adaptive information 1632 and/ornon-adaptive information 1634. Recall from FIG. 4A that adaptiveinformation 1632 is content, structural elements, objects, information,or computer software that has been adaptively self-modified 905, 935 bythe adaptive computer-based application 925 based, at least in part, onusage behaviors 920 of process participants 200. Non-adaptiveinformation 1634 denotes any other information, content, objects, orcomputer software encompassed by the adaptive computer-based application925 that has not been adaptively self-modified 905, 935.

The process participant interface 1600 may also contain formattedinstances 1640 of other information such as information derived fromother content 180 a and other computer applications 181 a that arerelevant to process participants 200.

Formatted instances 1610, 1620 of adaptive recommendations 910 andformatted instances of adaptive computer application information 915 maycontain explicit educational or training information or content, orrelevant references or “help” information, in addition to more generalinformation or content relevant to the associated process. In someembodiments, the adaptive computer-based application 925 may include orinteract with a learning management system that may provide guidance onthe appropriate educational or training information to include in theadaptive recommendations 910.

Innovation Networks

According to some embodiments, the adaptive process 900 may be used tocreate adaptive “innovation networks” that may be applied to facilitatecollaborative research and development processes. These processes may beapplied within an organization, or span an unlimited number oforganizations or individuals. In some embodiments, adaptive recombinantprocesses may utilize the systems and methods of PCT Patent ApplicationNo. PCT/US05/001348, entitled “Generative Investment Process,” filed onJan. 18, 2005, which is hereby incorporated by reference as if set forthin its entirety, to enable innovation networks and processes.

FIG. 39 illustrates an adaptive process 900C, or innovation networkprocess, including the adaptive computer-based application 925, whichincludes the adaptive system 100. The structural aspect 210 of theadaptive system 100 encompasses an innovation map 1700, which associatesopportunities 1710 to capability components 1730, shown in FIG. 39organized within capability component categories or types 1720.Opportunities, capability component types, and capability components maybe collectively termed the “elements” of innovation map 1700. It shouldbe understood that although the innovation map 1700 is depicted in FIG.39 in a table format, the innovation map 1700 may be organized innetwork structure, including a fuzzy network structure. Further, theinnovation map 1700 may be incorporated within a process network, suchas in FIG. 25 (not explicitly shown in FIG. 39) within the structuralaspect 210.

“Opportunities,” as defined herein are ideas that can potentiallygenerate value and that involve investments of time, resources, orfinancial commitments. These opportunities may be within definedprocesses, such as business development and growth processes, commercialventure capital, corporate venturing processes, business incubationprocesses, marketing processes, research and development processes, andinnovation processes, or the investment processes and associatedactivities may be more ad hoc in nature. Typically, opportunities 1710consist of a bundle of two or more capability components, such as “cc 5”and “cc 7” 1730. For example, even if a business idea (opportunity) isbased on a technological break-through, the overall business ventureidea is likely to also include other differentiating components, such asprocesses (e.g., marketing processes). It is the uniqueness of thebundle of components that typically provides the economic value-creatingpotential of the idea.

Capability components 1730 may include both tangible and intangibleaspects of an opportunity 1710. The capability components 1730 mayconstitute a mutually exclusive, collectively exhaustive set for eachopportunity 1710. (The term collectively exhaustive, as used herein,means that the elements of a set comprise the totality of the set.) Or,the capability components 1730 may represent just a subset of theopportunity 1710 defined and may simultaneously be represented inmultiple opportunities 1710. A myriad of possibilities exist forrepresenting opportunities 1710 using capability components 1730.

The capability components 1730 of the innovation map 1700 are individualinstances of capability component categories or types 1720. Capabilitytypes 1720 may include, but are not limited to, products (includingprototypes), technologies, services, skills, relationships, brands,mindshare, methods, processes, financial capital and assets,intellectual capital, intellectual property, physical assets,compositions of matter, life forms, physical locations, and individualor collections of people.

The objective of any innovation process is to maximize the volume ofhigh value opportunities 1710 generated at the lowest possible cost.Meeting this objective is a function of multiple variables. One variableis the volume, breadth and quality of the capability components 1730.Another variable is the ability to combine capability components in alarge variety and novel ways. A third variable is the degree to whichthe greatest diversity of human attention to be applied, and applied inthe right places. The adaptive process 900C can be used to enableprocesses that beneficially affect these key variables of innovationprocess success.

The adaptive computer-based application 925, together with theinnovation map 1700, enables more effective innovation-based processesin several ways. First, elements of the innovation map 1700 may includeadaptive recommendations 250 that are delivered to process participants200. This approach can help make process participants 200 aware ofparticularly relevant elements of the innovation map 170. Second, theadaptive recommendations function 240 may be applied to modify 905 theinnovation map 1700 based on, at least in part, inferences on processparticipant 200 preferences or interests. This can facilitate theefficient development and maintenance of a collective innovation mapthat can most beneficially serve the interests of the processparticipants 200, including maximizing the number of high valueopportunities generated within innovation map 1700. Third, elements ofthe innovation map 1700 may be syndicated, modified, and recombinedamong process participants 200 through the application of the adaptiverecombinant system 800, enabling multiple, distributed innovation mapinstances. This structure can facilitate both shared and privateinnovation maps, effectively balancing the advantages of economies ofscale and local interests. The adaptive recombinant system approaches ofFIGS. 47, 48, 49A, and 49B may be applied to the syndication,modification, and recombination of elements of innovation map 1700.

The adaptive computer-based application 925 may contain, or interactwith, auxiliary functions (not shown in FIG. 39) that may additionallyfacilitate innovation processes. For example, the adaptivecomputer-based application 925 may contain functions to enable automaticor semi-automatic evaluation of opportunities 1710, to automatically orsemi-automatically generate additional opportunities 1710 throughcombinatorial operations on capability components 1730, and/or tofacilitate effective information gathering or experimental designassociated with uncertainties with regard to capability components 1730or other elements of innovation map 1700. These additional functions mayall be managed within an adaptive process network, such as the adaptiveprocess network of FIG. 25 within the structural aspect 210 of theadaptive system 100.

Adaptive Publishing

The adaptive process 900 may be applied to enable adaptive publishingsystems and processes. The adaptive process 900, when applied to enableadaptive publishing systems and processes, may generate adaptive analogsto non-adaptive “broadcasted” media such as print publications, radioprograms, music albums or soundtracks, television programs, films, orinteractive games; as well as generating adaptive media that may nothave specific broadcast analogs. In some embodiments, the methods andsystems defined by U.S. patent application Ser. No. 10/715,174, entitled“A Method and System for Customized Print Publication and Management,”may be integrated with adaptive recombinant processes to enable anadaptive publishing process.

FIG. 40 depicts an adaptive process 900D, or adaptive publishingprocess, according to some embodiments. An adaptive publishing function2000 that is included within the adaptive computer-based application 925(although in other embodiments, the adaptive publishing function 2000may be external to the adaptive computer-based application 925) receivesinput from the adaptive system 100. The input may be in the form ofadaptive recommendations 940 suitable for the adaptive publishingpurposes, generated from adaptive recommendations 250, or the input maybe in the form of informational content 2031 contained in the contentaspect 230 of the adaptive system 100. The content originating from thecontent aspect 230 may have been modified 935 by the adaptiverecommendations function 240. In either case, the adaptive publishingfunction 2000 uses the inputs from the adaptive system 100 to generatemedia that is appropriately customized for the recipients of the media200, 260. This customization of an adaptive publication, or mediainstance, may include the specific elements of content that will becontained in a media instance, and also the arrangement of the elementsof content in the media instance. Thus, a media instance, as usedherein, is a distinct set of objects or information in combination witha unique arrangement of the objects or information. The customization ofmedia into specific media instances is performed on the basis ofinferred media recipient 200, 260 preferences and interests, which arein turn based on recipient interactions with the adaptive system 100, orthrough inferred affinities between the media instance recipient andother individuals that have interacted with adaptive system 100.

As shown in FIG. 40, the adaptive publishing function 2000 generates oneor more instances of media 2030, adapted appropriately to thepreferences or interests of the media recipients 200, 260. Each mediainstance contains one or more elements of content, some or all of whichmay be objects 212 or information 232 (FIG. 9A) contained in theadaptive system 100. Although not shown explicitly in FIG. 40, a mediainstance may also explicitly or implicitly include relationships amongobjects 214 associated with the structural aspect 210 of the adaptivesystem 100.

As shown in the example of FIG. 40, media instance 2010 containsmultiple objects in a particular configuration, including “Object A”2012 and “Object D” 2014. Recall that the objects 212 of the adaptivesystem 100 may contain any form of digital information, including text,graphics, audio, video, and executable software. These objects may betransformed to alternative media forms by the adaptive publishingfunction 2000. An individual media instance can therefore be defined asa set of information objects 212 or information items 232 and aparticular arrangement of the objects of information items. So, as oneexample, on-line textual objects 212 may be transformed into printedmedia by the adaptive publishing function 2000. In the case of printedmedia, a specific media instance is determined by not only the objectsto be included in a media instance, but also the arrangement or printlayout of the objects 212 and any other content included within themedia instance. The information objects 212 within a media instance maybe substantive in nature, or non-substantive (e.g., promotional oradvertising information).

In accordance with inferred preferences and interests of the intendedrecipients, media instance 2020 contains a different set of objects anda different arrangement of objects than media instance 2010. Forexample, “Object A” 2012 exists in both media instance 2010 and 2020,but for example, “Object D” 2014 is unique to media instance 2010 and“Object E” 2024 is unique to media instance 2020.

Although the adaptive media instances 2030 of FIG. 40 depict differingarrangements of objects and other items of content in accordance with aspatial orientation, consistent with, for example, physicalspatially-oriented media such as printed media, including newsletters,newspapers, magazines, and books, it should be understood that thecustomized object selection and arrangement of the adaptive publishingfunction 2000 may apply to other media types as well. In such cases, thearrangement of elements of the media instance may be other than spatialin nature; for example, the arrangement may be temporal-based for mediacontaining information than is typically “consumed” sequentially. Forexample, for audio objects 212 or information 232 such as songs, thespecific songs selected, and arrangement of the songs in a sound trackmay be different across media instances. For video or multi-mediaobjects 212 or information 232, customized media instances may includeapplying the adaptive publishing function 2000 to choose differentmusical sound tracks for corresponding elements of video, or evengenerating different media instances containing different elements of,or a different sequence of, the plot or story line of the video. Forinteractive media, such as computer-based games, the game instance maybe customized by the adaptive publishing function 2000 through theselection of different software modules of the game, or by arranging thesoftware modules of the game in different ways in different mediainstances.

For audio and/or video-based objects 212 or information 232, theadaptive publishing function 2000 may generate media instances thatconstitute “programs,” which are adaptive analogues of radio programs,television programs or other broadcasted forms.

Media instances may be delivered or otherwise made available 2002 toprocess participants 200, or made available 2004 to non-processparticipants 260. Media instances may take a purely digital form thatcan be embodied in a variety of physical forms. They may be available torecipients in the purely digital form, or they may be available toprocess participants 200, or to non-process participants 260 throughother physical embodiments. A media instance may be printed, forexample. A media instance may be stored on portable storage media suchas CD-ROMs or DVD's.

The adaptive publishing function 2000 of the adaptive process 900D mayapply additional logic or information in generating adaptive mediainstances 2030 that may not be available from the usage aspect 220 ofthe adaptive system 100. For example, a record of what objects 212 orinformation 232 have been contained in media instances received byparticular recipients may be used to ensure that a recipient does notreceive another media instance that contains information the recipientis likely to have already seen or heard. (This rule might be relaxed oradjusted, for example, for non-substantive content that is included foradvertising or promotional purposes.) The adaptive publishing function2000 may also include special capabilities for managing advertising orpromotional information within each media instance. These capabilitiesseek to optimize or to control advertising or promotional content suchthat the content will be of the most value to consumers or producers ofthe media instances 2030, while aligning the frequency and prominence ofthe advertising or promotional information with the terms and conditionsagreed to by suppliers of the advertising or promotional content. Theadvertising or promotional content may exist within the adaptive system100, or may be managed within the adaptive publishing function 2000.

The adaptive publishing function 2000 may apply other globalconsiderations or rules in generating adaptive media instances. Forexample, limits on the amount of information within a media instance mayinfluence the composition of the media instances. The informationallimits may be measured, for example, in terms of the number of words ornumber of pages for text-based media, or, for example, by duration foraudio or video-based media. Furthermore, there may be limits on thenumber of unique media instances generated, and in this case theadaptive publishing function 2000 may apply optimization algorithms todetermine media instance composition and arrangement so as tocollectively benefit media recipients 200, 260 while conforming to thelimits on the number of unique media instances.

The adaptive publishing function 2000 may also apply specific formattingfeatures to media instances. For example, for text-based mediainstances, specific fonts, font-size, colors, line spacing, and otherformat variations may be applied in accordance with inferred preferencesof media recipients 200, 260.

The adaptive publishing function 2000 may also apply alternativelanguages to media instances in accordance with inferred preferences ofmedia recipients 200, 260.

Although not explicitly shown in FIG. 40, information regarding mediainstances and the corresponding recipients within the adaptivepublishing function 2000 may be made available to the adaptive system100, and constitute another behavioral aspect incorporated by the usageaspect 220, that can be used by the adaptive recommendations function240 in generating subsequent recommendations.

Adaptive Commerce

Adaptive processes may be employed to recommend products or services 910based not only on customer 200 buying behaviors and patterns, but alsowithin the context of auxiliary information or rules that may bespecific to the customer or potential customer 200, the customer'sorganization, and/or the products and services purchased.

According to some embodiments, FIG. 41 depicts an adaptive process 900E,or adaptive commerce process, which includes the functions of anadaptive commerce application. A commerce contextualization function2100 within the adaptive computer-based application 925 providesadditional contextualization to the adaptive system 100 for use by theadaptive recommendations function 240. The commerce contextualizationfunction 2100 may deliver information to the adaptive system 100directly 2141 to the adaptive recommendations function 240, or throughinformation transfer 2140 to the usage aspect 220. It should beunderstood that the commerce contextualization function 2100 may beexternal to the adaptive computer-based application 925, in someembodiments, and transfer information to the adaptive computer-basedapplication 925; which may, in turn, transfer the information to theadaptive system 100. It should also be understood that although thecommerce contextualization function 2100 is shown in FIG. 41 to beexternal to the adaptive system 100, some or all of the functions ofcommerce contextualization function 2100 could alternatively be internalto the adaptive system 100. For example, some or all of the informationassociated with the commerce contextualization function 2100 could bedirectly derived from process participant behaviors 920 and stored andprocessed in usage aspect 220.

The commerce contextualization function 2100 of the adaptive process900E includes one or more functional elements, each of which may includerelevant information and procedures or algorithms. As shown in FIG. 41,the commerce contextualization function 2100 may include a customercontext function 2110, a purchase history function 2120, and aproduct/service attributes function 2130. The customer context function2110 includes contextualization information associated with thecommercial process participants 200, or customers, that are notavailable through inferences from customer behaviors 920. For example,for business customers, the customer context function 2110 may includeinformation regarding office site and layout or other businessenvironment-related information. Such information may prove useful inproviding recommendations 910 that are most relevant given the businessenvironment of the customer. As another example, pertaining torecommendations to consumers, the customer contextualization function2110 may contain information on family members of a particular customer,including gender, age, etc., thereby enabling tuning of recommendations910 (as one example, in the case of gift recommendations) appropriately.

The commerce contextualization function 2100 may also, or alternatively,include a purchase history function 2120. This function includes amapping of customers to purchases of products or services over time.This information can be used by the adaptive recommendations function240 to deliver more effective recommendations 910. For example, purchasepatterns that are embedded in the information associated with thepurchase history function 2120, combined with usage behaviors 920, mayenable the recommendation function 240 to generate improvedrecommendations 910 through incorporation of insights associated withpurchase timing patterns. For example, it may be determined byapplication of the purchase history function 2120 that a certainbusiness customer buys certain products only twice a year, and always inconjunction with another product type. The recommendations 910 may thenbe appropriately aligned with this pattern.

The commerce contextualization function 2100 may also, or alternatively,include a product or service attributes function 2130. This functionincludes additional information or context for product or services. Asan example, for durable products or goods, a schedule of depreciationmay be included in the product/service attributes function 2130. Suchinformation may enable the adaptive recommendations function 240 to tunerecommendations to be consistent with the expected lifespan ofpreviously purchased products.

The customer context function 2110, the purchase history function 2120,and the product/service attributes function 2130 may be appliedindependently, or collectively, in providing additional information toadaptive system 100 to be used by the adaptive recommendations function240.

Adaptive commerce applications may be applied to adaptive pricediscovery processes, so as to more advantageously determine prices forproducts or services. Thus, an adaptive process 900F, or adaptive pricediscovery process, is depicted in FIG. 42, according to someembodiments. In addition to the commerce contextualization function2100, the adaptive computer-based application 925 may include, or haveaccess to, a price discovery function 2150 that provides input to theadaptive recommendations function 240 of the adaptive system 100.

Process participant behaviors 920 may be used to infer conscious orunconscious intensity of desire for a product or service, or acollection of products or services. Based on these inferences, as wellas information or rules 2151 from the price discovery function 2150, andoptionally, information from the commerce contextualization function2100, the adaptive recommendations function 240 generates adaptiverecommendations 910 that include prices for products or services that,in some embodiments, are optimized to yield the highest price that isexpected to achieve a sale of the associated product or service to theprocess participant 200. In other words, the price may be set at a levelthat is expected to maximize the seller's capture of the buyer'seconomic rent. The process participant behaviors and associatedinferences may be transferred 2152 from the adaptive recommendationsfunction 240 to the price discovery function 2150. Other contextualinformation may be applied by the combination of the price discoveryfunction 2150 and the adaptive recommendations function 240 to priceappropriately. For example, the price optimization may be adjusted asappropriate based on whether the sales transaction is expected toconstitute a one-time relationship, or whether future transactions maytake place. The results from the recommended prices 910 may be used todetermine inferred price sensitivities and elasticities 2155 for one ormore process participants 200. Thus, the price discovery function 2150may supply useful information 2151 to the adaptive recommendationsfunction 240, enabling optimal product pricing; likewise, the adaptiverecommendations function 240 may supply useful information 2152 to theprice discovery function 2150 for determining prices, priceelasticities, or other pricing functions.

The price discovery function 2150 may include a price discoveryexperimental design function that is applied to optimize the testing ofprices through the adaptive system 100. Hence, the combination of theprice discovery function 2150 and the adaptive system 100 can constitutea “closed” loop adaptive pricing function that applies insights onprocess participant 200 behaviors 920 to adjust pricing. In someembodiments, the price discovery function 2150 may apply the methods andsystems described in U.S. Provisional Patent Application Ser. No.60/652,578, entitled “Adaptive Decision Process.”

The adaptive price discovery function 2150 may employ price discoveryand pricing methods other than setting a fixed price for a product orservice. For example, the function 2150 may apply a bidding processes inwhich multiple process participants 200 bid on the product or service,or other collective price formation that utilize direct or indirectinteractions among process participants 200.

The adaptive price discovery function 2150 may utilize other suppliercontextual information to establish prices. This information may beaccessed directly from the commerce contextualization function (notshown), or from 2152 the adaptive recommendations function 240. Thisinformation may include the associated inventory level, production cost,production plan, and/or other supply chain considerations that may berelevant in establishing price levels for a product or service.

This adaptive pricing approach of the adaptive process 900F may beparticularly applicable in setting prices for collections, combinationsor “bundles” of products and services that may be specific or evenunique to a given customer or set of customers 200. The uniqueness ofthe bundle enables the provision of a maximum value-add to the customerby fine-tuning a recommended “solution” to a perceived customer needthat is comprised of multiple products or services. Such a customizedsolution can increase the value, or economic rent, to the customer. But,the uniqueness of the bundle also decreases the ability of the customerto “comparison shop,” and this reduced transparency enables the supplierto potentially capture a greater portion of the value-add of thecustomer. Hence, there is an opportunity for the supplier to create morevalue for customers and to prominently share in the increased value.

FIG. 43 depicts an adaptive process 900G, or adaptive commercialsolutions process. In addition to featuring the adaptive system 100,commerce contextualization function 2100, and price discovery function2150, the adaptive process 900G includes a product and/or servicebundling function 2160 within the adaptive computer-based application925. (A specific product/service bundle or combination may be termed a“solution.”) The product/service bundling function 2160 providesinformation 2161 a to the adaptive recommendations function 240 toenable adaptive recommendations 910 to include product/service bundlesor solutions to process participants 200 that are expected to berelevant or compelling to the process participants 200. Likewise, theadaptive recommendations function 240 provides information 2161 bassociated with inferences on the preferences or interests of processparticipants or customers 200. The product/service bundling function2160 may be applied in concert, or interact with 2162, the pricediscovery function 2150; together comprising a solution development andpricing process. The adaptive recommendations function 240 may combineinputs from the product/service bundling function 2160, the pricediscovery function 2150, and the commerce contextualization function2100, along with information from the usage aspect 220 in generatingrecommendations that may include solutions and associated pricing.

The product/service bundling function 2160 may provide suggested productor service configurations 2161 a, in addition to, or instead of, productand service bundle suggestions or options 2161 a. The term“configurations” as used herein in conjunction with the product/servicebundling function 2160 denotes a set of product or service features. Forexample, various product components or features may be combined on acustomized basis for a specific customer or customers 200. One exampleis the customization of the configuration of a personal computer—aspecific CPU, with specific storage devices, peripherals, monitor type,etc., may be suggested by the product/service bundling function 2160based on information 2161 b on inferred preferences from the adaptiverecommendations function 240.

Continuing the example, the suggested customized personal computer maythen be bundled by the product/service bundling function 2160 with adigital camera and a special warranty that encompasses both the personalcomputer and the camera. This bundle of products and services may thenbe specially priced by the price discovery function 2150, with theentire bundle of products and services, the configurations of theproducts and services, and bundle pricing being informed by the inferredpreferences and interests of process participants (customers) 200.

The product/service bundling function 2160 and adaptive price discoveryfunction 2150 may be applied together to create a bidding process forproduct/service bundles. The product/service bundling function 2160 maygenerate bundles or solutions applicable to multiple processparticipants 200, and the adaptive price discovery function 2150 is usedto organize and manage the bids. The adaptive computer-based application925 may use the adaptive system 100 and the product/service bundlingfunction 2160 to determine the best mix of bundles and processparticipants to maximize the value of the auction.

The product/service bundling function 2160 and adaptive price discoveryfunction 2150 may utilize other supplier contextual information toestablish solutions and associated prices. This information may beaccessed directly from the commerce contextualization function (notexplicitly shown in FIG. 43), or indirectly from 2152, 2161 b theadaptive recommendations function 240. This supplier contextualinformation may include the associated inventory level, production cost,production plan, and/or other supply chain considerations that may berelevant in establishing price levels for one or more products orservices, and/or configurations thereof.

Location-Aware Adaptive Sales and Marketing

Recall from Table 1 that process participant behaviors 920 may includebehaviors associated with physical location, and the movement amongphysical locations, of process participants 200. According to someembodiments, the adaptive process 900 may be applied to enable sales orprocurement-related processes in which the sales processes of apotential supplier monitor physical locations of potential customers 200and deliver adaptive recommendations 910 that are appropriatelycontextualized for the customer's location, or location history.Further, the customers or potential customers 200 may themselves employsystems that interact at varying levels of interaction and cooperationwith the supplier's sales processes. Where both the supplier and thepotential customers employ adaptive recombinant processes and thepotential customer and/or the potential supplier is mobile, alocation-aware collectively adaptive system and associatedlocation-aware collectively adaptive commercial process 900H is enabled

FIG. 44 depicts a location-aware collectively adaptive process 900H,including a location-aware collectively adaptive system 2200. Fourseparate instances of adaptive computer applications within system 2200are shown; each instance corresponds to an instance of the adaptivecomputer-based application 925 of FIGS. 4A and 4B. Two of the instancesare mobile adaptive computer applications; a first mobile adaptivecomputer-based application 925 m 1, and a second mobile adaptivecomputer-based application 925 m 2. Two of the instances are stationaryadaptive computer applications, a first stationary adaptivecomputer-based application 925 s 1, and a second stationary adaptivecomputer-based application 925 s 2. Each of the adaptive computer-basedapplication instances may interact with any of the other instances, asdepicted by the flow of information 2210 between the first stationaryadaptive computer-based application instance 925 s 1 and the firstmobile adaptive computer-based application instance 925 m 1.

The information flow 2210 between any two adaptive computer-basedapplication instances of the location-aware collectively adaptive system2200 may include the following:

-   -   1) Polling and detection of a second adaptive computer-based        application instance by a first adaptive computer-based        application instance.    -   2) Identifying the detected second adaptive computer-based        application instance by the first adaptive computer-based        application instance.    -   3) Determining a mutual contextual basis for further        interaction—that is, either a) from the potentially        supplier-side adaptive computer-based application instance,        determining whether the potential receiving or customer-side        adaptive computer-based application instance encompasses a        customer context or set of inferred preferences or interests        that would enable one or more relevant recommendations 910 to be        generated for the process participants 200 of the customer-side        adaptive computer-based application instance; or b) from the        potentially receiving or customer-side adaptive computer-based        application instance, determining whether the supplier-side        adaptive computer-based application instance encompasses a        supplier context and product or service attributes that would        enable an expected one or more relevant recommendations 910 to        be generated for the process participants 200 of the        customer-side adaptive computer-based application instance. This        determination of a mutual contextual basis for further        interaction may be made by one or the other, or both instances.    -   4) Receiving from, or supplying to, the second adaptive        computer-based application instance contextualized information        that enables either a) the adaptive recommendations 910 of the        first adaptive computer-based application instance to        selectively utilize the contextualized information of the second        adaptive computer-based application instance; or b) enables the        adaptive recommendations 910 of the second adaptive        computer-based application instance to selectively utilize the        contextualized information of the first adaptive computer-based        application instance.        It should be noted that the interactions 2210 may occur between        any two adaptive computer-based applications 925. For example,        the interactions 2210 may be between two stationary adaptive        computer-based application instances, such as the information        flow 2250 between instance 925 s 1 and instance 925 s 2. Or the        information flow 2230 may be between two mobile adaptive        computer application instances, such as instance 925 m 1 and        instance 925 m 2. Finally, the interactions 2220 may be between        a stationary adaptive computer-based application instance 925 s        1 and a mobile adaptive computer-based application instance 925        m 2.

According to some embodiments, FIGS. 45 and 46 depict two examples oflocation-aware collectively adaptive systems 2200. FIG. 45 (2200A)provides additional details regarding the constituent adaptive computerapplication instances, and the interactions among the instances, of thelocation-aware collectively adaptive system 2200 of FIG. 44. Astationary adaptive computer application instance 925 s includes anadaptive system 100 and a supplier commerce contextualization function2300 (see FIG. 43). The supplier commerce contextualization function2300 is comprised of one or more of 1) a supplier context function 2310,2) a purchase history function 2120, and 3) a product and serviceattribute function 2130. Although not shown in FIG. 45, the suppliercommerce contextualization function 2300 may also include a customercontext function 2110. The supplier context function 2310 includescontextual information about the potential supplier that is utilizing orapplying the adaptive computer-based application instance 925 s, that isnot contained in product and service attributes function 2130. Forexample, supplier context function 2310 may include the physicallocation of the supplier, the hours of business, the history of thebusiness, and any other information that may be relevant to a customeror prospective customer. The adaptive system 100 of the adaptivecomputer-based application 925 s interacts 2305 with the suppliercommerce contextualization function 2300, as desired, to delivereffective adaptive recommendations 910 s to process participants 200 s.

The stationary adaptive computer-based application instance 925 sinteracts 2415 with the mobile adaptive computer-based applicationinstance 925 m. The mobile adaptive computer-based application instance925 m includes an adaptive system 100 and a mobile customer commercecontextualization function 2400. The mobile customer commercecontextualization function 2400 includes one or both of a 1) customercontext function 2110 and 2) a preferences and interests function 2420.The preferences and interests function 2420 contains inferredpreferences and interests of process participants 200 m based on theirinteractions with adaptive system 100.

The stationary adaptive computer-based application instance 925 sinitially interacts 2415 with the mobile adaptive computer-basedapplication instance 925 m through an initial detection by one or theother of the instances, or through mutual detection. Next, aninteraction 2425 is invoked that seeks to establish a basis forcommercial interaction between the two instances. Information frommobile customer commerce contextualization function 2400 is compared toinformation in the supplier commerce contextualization function 2300. Sofor example, a service station employing instance 925 s detecting amobile process participant 200 m that is a child riding a bicycle isunlikely to have a basis for initiating a commercial interaction, andtherefore interactions would cease, whereas if the mobile processparticipant 200 m was a truck driver driving a truck that was due forservice, then a basis for commercial interaction may exist.

The adaptive computer-based application instances 925 s, 925 m may applylocation information, or inferences derived from location and time, inestablishing a context for commercial interaction or for generation ofadaptive recommendations within the location-aware collectively adaptivesystem 2200. The adaptive computer-based application instances 925 s,925 m may utilize geographic-related context or information such asthrough access to digitized maps in making inferences from location andtime information associated with process participants 200.

For example, the respective physical locations of two or more instancesmay be a determinant of a basis for commercial interaction or forgenerating adaptive recommendations. The prospective customer orprospective supplier may have thresholds of distance that may be appliedto determine a basis for commercial interaction. This threshold distancemay be in absolute terms, or in terms of expected transit time between amobile adaptive computer-based instance and a stationary instance oranother mobile instance. Inferred direction and speed of a mobileinstance may be calculated and used as input to establishing context forcommercial interaction or for generating adaptive recommendations.Further, the inferred mode of transportation of the mobile processparticipant 200 may be a determinant for commercial interaction orgeneration of recommendations, as such information may affect theexpected transit time or ease of access to the supplier.

Assuming that a basis for commercial interaction is established, a nextlevel of interaction 2435 may be established between the two instances925 m, 925 s. The preferences and interests 2420 of the mobile adaptivecomputer-based instance 925 m are accessed by the stationary adaptivecomputer-based instance 925 s to determine if there is a basis forproviding suggested products or services to the mobile adaptive computerinstance 925 m. If the supplier commerce contextualization function 2300determines that there is a basis for suggesting or recommendingproducts, then these are transmitted 2445 to mobile adaptive computerapplication instance 925 m.

The suggested products or services 2445 are incorporated by the adaptiverecommendations function 240 of the adaptive system 100 of mobileadaptive computer-based application 925 m in generating recommendations910 m to process participants 200 m.

FIG. 46 (2200B) illustrates that the mobile adaptive computer-basedapplication instance 925 m, along with the associated processparticipants 200 m, may be considered the process participants 200 sm ofthe stationary adaptive computer-based application instance 925 s. Theinteractions described in FIG. 45 are conducted through the processparticipant behaviors 920 transmission to the instance 925 s, andthrough the adaptive recommendations 910 s generated by instance 925 sand received by process participants 200 sm. Although in FIG. 46, therespective adaptive application instances 925 s, 925 m are stationaryand mobile, respectively, it should be understood that the example maybe reversed, or two stationary or two mobile instances may utilize thesame topology for interactions, as depicted in FIG. 46.

The location-aware collectively adaptive system 2200 and process 900H(FIG. 44) may be applied to a variety of sales and procurement processareas. For example, restaurants can apply such processes by providingprospective diners that are in the vicinity of relevant recommendedoptions, tuned to the prospective diner's particular preferences andtastes.

The location-aware collectively adaptive system 2200 and process 900Hmay further apply the adaptive price discovery systems and processes ofFIG. 42 or the adaptive commercial solutions systems and process of FIG.43.

A mobile adaptive computer application instance 82 bm 1 may be embodiedwithin a portable computing device, such as a mobile phone or personaldigital assistant (PDA). A mobile adaptive computer application instance82 bm 1 may be contained in mobile apparatus, such as vehicles or othertransportation devices. In some embodiments, a mobile adaptive computerapplication instance 82 bm 1 may reside within a self-propelled deviceor appliance.

Adaptive Viral Marketing

In the prior art, viral marketing techniques are known that promote theinitial recipients of a sales or marketing-related message to re-sendthe message to others. For example, viral marketing through e-mailmessages is a familiar technique. However, prior art viral marketingtechniques exhibit two significant limitations: 1) there is littleability for a recipient to easily modify the received message for thebenefit of others he or she will re-send the message to, and 2) thestructure of the message is typically a single item of informationembodied in a single computer file (such as a e-mail message, or a textdocument).

According to some embodiments, an application of adaptive recombinantprocess 901, adaptive recombinant process 901B, may be used toadvantageously transform customer relationships, promote sales,facilitate business development, enhance public relations or generallyincrease “share of mind.” In contrast to the prior art, through theapplication adaptive recombinant process 901B, content networks orprocess networks comprising multiple units of interconnected informationmay be syndicated to potential customers or individuals or institutionsfor whom influence is sought. The content or process networks may thenbe syndicated to the customer's customers or influence targets, and soon, potentially without limit. At each stage of syndication and receipt,one or more content or process networks may be modified or combined,optionally enabled by an adaptive recommendations function 240. Thecontent within the syndicated content networks may be substantive ornon-substantive (e.g., advertising or promotional content). Thisapplication of adaptive recombinant process 901B provides a much morepowerful and comprehensive approach to viral marketing and publicrelations than is possible with prior art approaches.

FIG. 47 illustrates an adaptive recombinant systems construct to managesyndication and recombination of network structures for a variety ofprocess purposes, including enabling adaptive viral marketing process901B. Recall from FIGS. 16 and 17 that the adaptive recombinantcomputer-based application 925R may include the adaptive recombinantsystem 800C, which in turn, may encompass the adaptive system 100C (FIG.14). In the embodiment of FIG. 47, the adaptive system 100C managesmultiple networks within the structural aspect 210C. These networks maybe content networks or process networks, and may be fuzzy networks. Forexample, some or all of “network 1” 2510 may be syndicated 2515 to“network 2” 2520 and combined, followed by some or all of the resultingnetwork combination syndicated 2525 to “network 3” 2530 and combinedwith “network 3” 2520. A closed loop may be formed, as some or all ofthis last network combination may be syndicated 2535 back to theoriginal “network 1” 2510 and combined with “network 1” 2510. Thisprocess may continue indefinitely. At each stage, it should beunderstood that a network may be syndicated to a recipient that does notpossess a network. Such a recipient may nevertheless modify the networkand re-syndicate. For each stage, the selection, syndication,modification, or combination is enabled by the functions of the adaptiverecombinant system 800C, as described previously. Thus, the adaptiverecommendations function 240 may be applied to facilitate thesesyndications, modifications, and combinations based, in part, oninferences of preferences and interests from the usage behaviors 920 ofprocess participants 200.

FIG. 48 illustrates an alternative adaptive recombinant systemsconstruct using an adaptive recombinant system 800 i to managesyndication and recombination of network structures for a variety ofprocess purposes, including enabling adaptive viral marketing process901B. Adaptive recombinant system 800 i includes multiple instances ofadaptive system 100 i. Although not shown in FIG. 48, each adaptivesystem instance, such as adaptive system 100 i 1, may have its ownindependent set of process participants 200, or the process participants200 of each adaptive system instance may overlap.

In the embodiment of FIG. 48, each adaptive system instance 100 imanages one or more networks within its structural aspect 210 (notshown). These networks may be content networks or process networks, andmay be fuzzy networks. As an example, some or all of the structuralaspect and/or usage aspect of the first adaptive system instance 100 i 1may be syndicated 2555 to a second adaptive system instance 100 i 2, andthe structural and/or usage aspects optionally combined. Some or all ofthe structural and/or usage aspects of the second adaptive systeminstance 100 i 2 may then be syndicated 2565 to a third adaptive systeminstance 100 i 3, and the structural and/or usage aspects optionallycombined. A closed loop may be formed, as some or all of the structuraland/or usage aspects of the third adaptive system instance 100 i 3 maybe syndicated 2575 back to the original adaptive system instance 100 i1.

Thus, the process of syndication, modification, and combination maycontinue indefinitely. At each stage, it should be understood that anentire adaptive system instance 100 i may be syndicated to a recipientthat does not have access to the adaptive system instance 800 i 1. Andat each stage, the selection, syndication, modification, or combinationis enabled by the functions of the adaptive recombinant system 800, asdescribed previously. Thus, the adaptive recommendations function 240 ofeach adaptive system instance 100 i may be applied to facilitate thesesyndications, modifications, and combinations based, in part, oninferences of preferences and interests from usage behaviors 920 ofprocess participants 200.

The systems and methods described in FIG. 47 and FIG. 48 may be appliedto enabling adaptive viral marketing process 901B, in some embodiments,as depicted in FIGS. 49A and 49B. In FIGS. 49A and 49B, the syndicationand recombination of content networks across organization are described.It should be understood that the content networks described may or maynot be fuzzy networks, and may or may not be process networks. It shouldalso be understood that the networks may include usage behavioralinformation associated with the usage aspect 220, in addition to, orinstead of content networks associated with structural aspect 210 c ofthe adaptive system 100. Further, although the syndication is to“organizations,” it should be understood that the term as used hereinmay include a single person.

FIG. 49A depicts a the selection or sub-setting of content network“network 1” 2735 residing in “organization 1” 2650 to form “network 1a”2695. “Network 1a” 2695 may contain substantive or non-substantiveinformation (such as advertising or promotional content), and issyndicated to “organization 2” 2655 for the purposes of either directpromotion, with an option for indirect promotion through re-syndicationby “organization 2” 2655; or the syndication to “organization 2” 2655may be for the primary or sole purpose of indirect promotion through“organization 2's” 2655 expected re-syndication of the network.

In this example, “network 1a” 2700 and the existing “network 2” 2705 in“organization 2” are combined 2710 to form “network 2a” 2715 in“organization 2” 2655. This combination may be either for the directbenefit of “organization 2” 2655, or the purposes of continuing thechain of promotion through re-syndication of a network of substantiveand/or non-substantive information that is expected to be increasinglyvaluable to each new generation of recipients.

Continuing the example, “network 2a” 2715 is then syndicated to“organization 3” 2660, wherein “organization 3” 2660 does not alreadypossess or have access to a content network.

FIG. 49B represents a continuation of FIG. 49A to depict the potentiallyclosed-loop aspect of the adaptive viral marketing process. “Network 2a”2725 in “organization 3” 2660 is syndicated to “organization 1” 2655.“Network 2a” 2725 is then combined with the original “network 1” 2735 in“organization 1” 2650 to generate “network 3” 2740 in “organization 1”2650.

FIGS. 49A and 49B demonstrate that, in some embodiments, the adaptiverecombinant process 901B may, without limit, enable sub-setting ofnetworks of substantive and/or non-substantive information, syndicatingthe subsets to one or more destinations, and enabling the syndicatednetworks to be combined with one or more process networks at thedestinations. At each combination step, functions of adaptiverecombinant system 800C may be applied, including the relationshipresolution functions and the adaptive recommendations function, tocreate and update process structure (and content) as appropriate. Theparticipants 200 in the adaptive viral marketing process may or may notbe directly conscious of playing a role in marketing or promotion.

As a specific example of the economics of viral marketing, theoriginator of the adaptive viral marketing process 901B may supply aproduct or service for which there are complementary products orservices; by complementary, it is meant that the supplier can sell moreof its product or services to a customer if the customer has access to,or can purchase, the complementary products or services. So, forexample, commentary by other process participants, particularly processparticipants with special expertise of relevant reputation, may be acomplement to selling a tangible or intangible product, such as a video.Through the initiation of the viral marketing approach, delivery ortargeted, complementary commentary may be efficiently achieved thatcould stimulate greater demand for the video itself.

The adaptive viral marketing process 901B of FIGS. 49A and 49B may alsoapply methods associated with location-aware collectively adaptivesystem 2200 and process 900H, and may further apply the systems andmethods of the adaptive commercial solutions process (900G) depicted inFIG. 43.

Evolvable Processes

According to some embodiments, the adaptive recombinant process 901 maybe used to deploy an evolvable process 901E across one or moreorganizations or environments. FIG. 50 depicts an embodiment of theadaptive recombinant computer-based application 925R of FIG. 4C, whichincludes an evolvable adaptive recombinant system 800 e, which itselfincludes the adaptive recombinant function 850. The adaptive recombinantfunction 850 in turn includes a syndication function 810, a fuzzynetwork operators function 820, and an object evaluation function 830,all of which were described previously. The evolvable adaptiverecombinant system 800 e also contains one or more instances 100 i ofthe adaptive system 100. Process participants 200 generate process usagebehaviors 920 that are tracked and processed by the one or more adaptivesystem instances 800 i. In addition, the evolvable adaptive recombinantsystem 800 e contains a network evaluation function 860, which is usedto evaluate the “fitness” of one or more content networks, which mayinclude process networks, and works in concert 2905 with the adaptiverecombinant function 850 to generate new generations of content networksfrom a previous generation of content networks deemed to be most fit bythe network evaluation function 860.

Recall from FIG. 47 that an instance of the adaptive system 100 maycontain multiple content networks. The network evaluation function 860may evaluate 2915 one or more networks within an adaptive systeminstance 100 i 3. The adaptive recombinant function 850 may then beapplied to create a new generation of recombinant content networkswithin the adaptive system instance 100 i 3, based on the individualfitness of the previous generation of content networks.

Alternatively, the network evaluation function 860 may evaluate 2935content networks across adaptive systems instances 100 i. The adaptiverecombinant function 850 may then be applied to create a new generationof recombinant content networks across adaptive system instances 100 i,based on the individual fitness of the previous generation of contentnetworks across system instances 100 i.

The network evaluation function 860 may apply criteria derived frominferences on preferences and interests of usage behaviors 920 ofprocess participants 200. These criteria may be augmented by additionalevaluation criteria and logic as required.

The adaptive recombinant function 850 may generate new generations ofcontent networks based on purely the inheritance of characteristicsderived from combinations of previous generations of content networks(Lamarkian approach to network evolution), and/or the adaptiverecombinant function 850 may apply random changes to the contentnetworks, so as to create network mutations, which, in turn, increasesnetwork variation (Darwinian approach to network evolution). Geneticalgorithms may be applied to generate network mutations andcombinations.

Evolvable adaptive recombinant system 800 e can therefore enable theevolvable process 901E, which can serve as a means of accelerating thedevelopment of the most adaptive possible processes for a givenorganizational environment.

Computing Infrastructure

FIG. 51 depicts various hardware topologies that the adaptive process900, the adaptive recombinant process 901, the adaptive computer-basedapplication 925, the adaptive recombinant computer-based application925R, the adaptive system 100, or the adaptive recombinant system 800may embody. Further, the adaptive asset management process 900A, theadaptive real-time learning process 900B, the innovation network process900C, the adaptive publishing process 900D, the adaptive commerceprocess 900E, the adaptive price discovery process 900F, the adaptivecommercial solutions process 900G, the location-aware collectivelyadaptive process 900H, the recombinant process network process 901A, theadaptive viral marketing process 901B, the evolvable process 901E, orother applications of the adaptive process 900 or adaptive recombinantprocess 901 not described herein may utilize the hardware and computingtopologies of FIG. 51. These various systems are referred to as the“relevant systems,” below.

Servers 950, 952, and 954 are shown, perhaps residing at differentphysical locations, and potentially belonging to different organizationsor individuals. A standard PC workstation 956 is connected to the serverin a contemporary fashion. In this instance, the relevant systems, inpart or as a whole, may reside on the server 950, but may be accessed bythe workstation 956. A terminal or display-only device 958 and aworkstation setup 960 are also shown. The PC workstation 956 may beconnected to a portable processing device (not shown), such as a mobiletelephony device, which may be a mobile phone or a personal digitalassistant (PDA). The mobile telephony device or PDA may, in turn, beconnected to another wireless device such as a telephone or a GPSreceiver.

FIG. 51 also features a network of wireless or other portable devices962. The relevant systems may reside, in part or as a whole, on all ofthe devices 962, periodically or continuously communicating with thecentral server 952, as required. A workstation 964 connected in apeer-to-peer fashion with a plurality of other computers is also shown.In this computing topology, the relevant systems, as a whole or in part,may reside on each of the peer computers 964.

Computing system 966 represents a PC or other computing system, whichconnects through a gateway or other host in order to access the server952 on which the relevant systems, in part or as a whole, reside. Anappliance 968, includes software “hardwired” into a physical device, ormay utilize software running on another system that does not itself hostthe relevant systems. The appliance 968 is able to access a computingsystem that hosts an instance of one of the relevant systems, such asthe server 952, and is able to interact with the instance of the system.

The relevant systems may utilize database management systems, includingrelational database management systems, to manage to manage associateddata and information, including objects and/or relationships amongobjects. The relevant systems may apply intelligent “swarm” peer-to-peerfile sharing techniques to facilitate the syndication of large networksof content, by enabling a plurality of peer computing devices tocollectively serve as file servers, thus acting to de-bottleneck thesharing of large networks of information. Further, adaptive recombinantprocesses may apply intelligent swarm peer-to-peer sharing to the entirenetwork of information (objects and relationships) that is to besyndicated, rather than just individual files. The relevant systems mayapply special algorithms to optimally syndicate elements of one or morenetworks of information across a plurality of peer computing devices toenable the collective set of peer computing devices to be utilized asservers in a manner to enable the most efficient syndication oflarge-scale networks of information.

While the present invention has been described with respect to a limitednumber of embodiments, those skilled in the art will appreciate numerousmodifications and variations therefrom. It is intended that the appendedclaims cover all such modifications and variations as fall within thescope of this present invention.

What is claimed is:
 1. A computer-implemented method, comprising:accessing automatically content that is associated with a mediainstance; performing automatically one or more inferences that are basedon analyzing the content by application of a computer-implemented neuralnetwork, wherein the neural network is a convolutional neural network;generating automatically a plurality of recommended objects based, atleast in part, upon the one or more inferences; delivering automaticallythe plurality of recommended objects to one or more users; accessingautomatically one or more usage behaviors associated with at least oneuser of the one or more users interacting with at least one recommendedobject of the plurality of recommended objects; selecting automaticallyan element of the media instance based on the one or more usagebehaviors; and delivering automatically the element of the mediainstance to a user of the one or more users.
 2. The method of claim 1,further comprising: accessing automatically the content that isassociated with the media instance, wherein the content comprises video.3. The method of claim 2, further comprising: performing automaticallythe one or more inferences, wherein the one or more inferences arefurther based on automatically analyzing audio content that isassociated with the video by application of a secondcomputer-implemented neural network, wherein the second neural networkcomprises a long short-term memory neural network.
 4. The method ofclaim 1, further comprising: performing automatically the one or moreinferences, wherein each of the one or more inferences is associatedwith a weight; generating automatically the plurality of recommendedobjects based, at least in part, upon the one or more inferences andeach of the associated weights.
 5. The method of claim 1, furthercomprising: performing automatically the one or more inferences, whereinthe one or more inferences each comprise a theme that is inferred fromthe content.
 6. The method of claim 1, further comprising: generatingautomatically the plurality of recommended objects, wherein therecommended objects are further generated in accordance with aninference of a preference that is based on a plurality of usagebehaviors associated with one or more users.
 7. The method of claim 6,further comprising: generating automatically the plurality ofrecommended objects, wherein the recommended objects are furthergenerated in accordance with the inference of the preference, whereinthe inference of the preference is based upon the application of aplurality of inference weightings that are determined in accordance withusage behavior priorities that are applied to the plurality of usagebehaviors that are associated with the one or more users.
 8. Acomputer-implemented system comprising one or more processors configuredto: access automatically content that is associated with a mediainstance; perform automatically one or more inferences that are based onanalyzing the content by application of a computer-implemented neuralnetwork wherein the computer-implemented neural network comprises arecurrent neural network; generate automatically a plurality ofrecommended objects based, at least in part, upon the one or moreinferences; deliver automatically the plurality of recommended objectsto one or more users; access automatically one or more usage behaviorsassociated with at least one user of the one or more users interactingwith at least one recommended object of the plurality of recommendedobjects; select automatically an element of the media instance based onthe one or more usage behaviors; and deliver automatically the elementof the media instance to a user of the one or more users.
 9. Thecomputer-implemented system of claim 8 further comprising the one ormore processors configured to: access automatically the content that isassociated with the media instance, wherein the content comprises videoand associated audio; perform automatically the one or more inferencesthat are based on analyzing the associated audio by thecomputer-implemented neural network.
 10. The computer-implemented systemof claim 8 further comprising the one or more processors configured to:perform automatically the one or more inferences, wherein the one ormore inferences are based on automatically analyzing text that iscontained within the content, wherein the recurrent neural networkcomprises a long short-term memory neural network.
 11. Thecomputer-implemented system of claim 8 further comprising the one ormore processors configured to: perform automatically the one or moreinferences, wherein each of the one or more inferences is associatedwith a weight; generate automatically the plurality of recommendedobjects based, at least in part, upon the one or more inferences andeach of the associated weights.
 12. The computer-implemented system ofclaim 8 further comprising the one or more processors configured to:perform automatically the one or more inferences, wherein the one ormore inferences each comprise a theme that is inferred from the content.13. The computer-implemented system of claim 8 further comprising theone or more processors configured to: perform automatically the one ormore inferences, wherein the one or more inferences are performed inaccordance with an inference tuning control.
 14. Thecomputer-implemented system of claim 8 further comprising the one ormore processors configured to: generate automatically the plurality ofrecommended objects, wherein the recommended objects are furthergenerated in accordance with a computer-implemented structural aspectcomprising a plurality of affinities among a plurality ofcomputer-implemented objects, wherein the plurality of affinities aregenerated from a plurality of usage behaviors.
 15. Acomputer-implemented system comprising one or more processors configuredto: access automatically content that is associated with a mediainstance; perform automatically one or more inferences that are based onanalyzing the content by application of a computer-implemented neuralnetwork, wherein the computer-implemented neural network comprises aconvolutional neural network; generate a plurality of recommendedobjects based, at least in part, upon the one or more inferences;deliver automatically the plurality of recommended objects to one ormore users; access automatically a plurality of usage behaviorsassociated with at least one user of the one or more users, wherein theplurality of usage behaviors comprise behaviors associated with the atleast one of the plurality of users navigating the media instance;generate automatically a recommendation that is based upon an inferenceof a preference that is derived from the plurality of usage behaviors;and deliver automatically the recommendation to a user.
 16. Thecomputer-implemented system of claim 15 further comprising the one ormore processors configured to: perform automatically the one or moreinferences, wherein the one or more inferences are based onautomatically analyzing video by the computer-implemented neuralnetwork.
 17. The computer-implemented system of claim 15 furthercomprising the one or more processors configured to: generateautomatically the plurality of recommended objects, wherein therecommended objects are further generated in accordance with acomputer-implemented structural aspect comprising a plurality ofaffinities among a plurality of computer-implemented objects, whereinthe plurality of affinities are generated from a plurality of usagebehaviors.
 18. The computer-implemented system of claim 15 furthercomprising the one or more processors configured to: deliverautomatically the plurality of recommended objects to the one or moreusers, wherein the plurality of recommended objects are arranged in atemporal-based sequence.
 19. The computer-implemented system of claim 15further comprising the one or more processors configured to: generateautomatically the recommendation that is based upon the inference of apreference that is derived from the plurality of usage behaviors,wherein the inference of the preference is based upon the application ofa plurality of inference weightings that are determined in accordancewith usage behavior priorities that are applied to the plurality ofusage behaviors.
 20. The computer-implemented system of claim 15 furthercomprising the one or more processors configured to: deliverautomatically an explanation for delivering the recommendation, whereinthe explanation is in a natural language format and comprises reasoningfor the delivery of the recommendation.